In the 1960s molecular population geneticists used Monte Carlo experiments to evaluate particular diffusion equation models. In this paper I examine the nature of this comparative evaluation and argue for three claims: first, Monte Carlo experiments are genuine experiments: second, Monte Carlo experiments can provide an important meansfor evaluating the adequacy of highly idealized theoretical models; and, third, the evaluation of the computational adequacy of a diffusion model with Monte Carlo experiments is (...) significantlydifferent from the evaluation of the emperical adequacy of the same diffusion model. (shrink)

The term Monte Carlo method indicates any computer-aided procedure for numerical estimation that combines mathematical calculations with randomly generated numerical input values. Today it is an important tool in high energy physics while physicists and philosophers also often consider it a sort of virtual experiment. The Monte Carlo method was developed in the 1940s, in the context of U.S. American nuclear weapons research, an event often regarded as the origin of both computer simulation and (...) “artificial reality” (Galison 1997). The present paper interrogates this strong claim by focusing on the emergence of Monte Carlo event generators in particle physics in the early 1960s. This historical case study shows how, as Monte Carlo computation became part of the toolbox of particle physicists around 1960, it was neither usually referred to as a “computer simulation” nor was it regarded as a surrogate for experimentation. In revising the history of this method, this paper asks, in what context did particle physicists of the 1960s decide to create FAKE, the first high-energy-physics Monte Carlo event simulator? What was their goal? And what epistemic role did FAKE play? In answering these questions, it is argued that Monte Carlo computations were not introduced into particle physics to simulate experiments, but rather they played the role of theoretical tools. The Monte Carlo method was able to do this thanks to its random component, a property which provided a means of modeling a specific phenomenon, so-called “(particle) resonances”. Indeed, in doing so, event generators even came to mutually assimilate and reshape the notions of particle and resonance, taking up an epistemic function which had previously been confined to physical-mathematical formulae: that of a medium which could express aspects of particle theory. (shrink)

The term Monte Carlo method indicates any computer-aided procedure for numerical estimation that combines mathematical calculations with randomly generated numerical input values. Today it is an important tool in high energy physics while physicists and philosophers also often consider it a sort of virtual experiment. The Monte Carlo method was developed in the 1940s, in the context of U.S. American nuclear weapons research, an event often regarded as the origin of both computer simulation and (...) “artificial reality” (Galison 1997). The present paper interrogates this strong claim by focusing on the emergence of Monte Carlo event generators in particle physics in the early 1960s. This historical case study shows how, as Monte Carlo computation became part of the toolbox of particle physicists around 1960, it was neither usually referred to as a “computer simulation” nor was it regarded as a surrogate for experimentation. In revising the history of this method, this paper asks, in what context did particle physicists of the 1960s decide to create FAKE, the first high-energy-physics Monte Carlo event simulator? What was their goal? And what epistemic role did FAKE play? In answering these questions, it is argued that Monte Carlo computations were not introduced into particle physics to simulate experiments, but rather they played the role of theoretical tools. The Monte Carlo method was able to do this thanks to its random component, a property which provided a means of modeling a specific phenomenon, so-called “(particle) resonances”. Indeed, in doing so, event generators even came to mutually assimilate and reshape the notions of particle and resonance, taking up an epistemic function which had previously been confined to physical-mathematical formulae: that of a medium which could express aspects of particle theory. (shrink)

It was proposed that the data from Monte Carlo statistical investigations be subjected to analysis of variance methods rather than the conventional techniques of tabling, graphing, and inspecting the data. Two examples in which analysis of variance methods were applied to published Monte Carlo studies were presented. It was suggested that balanced factorial designs should be used whenever possible in Monte Carlo studies so that analysis of variance methods would be directly applicable. Finally, three (...) advantages of analysis of variance methods over conventional techniques were described and analysis of variance methods were contrasted with a possible alternative analytical method, multiple regression analysis. (shrink)

Exploring how people represent natural categories is a key step toward developing a better understanding of how people learn, form memories, and make decisions. Much research on categorization has focused on artificial categories that are created in the laboratory, since studying natural categories defined on high-dimensional stimuli such as images is methodologically challenging. Recent work has produced methods for identifying these representations from observed behavior, such as reverse correlation (RC). We compare RC against an alternative method for inferring the (...) structure of natural categories called Markov chain Monte Carlo with People (MCMCP). Based on an algorithm used in computer science and statistics, MCMCP provides a way to sample from the set of stimuli associated with a natural category. We apply MCMCP and RC to the problem of recovering natural categories that correspond to two kinds of facial affect (happy and sad) from realistic images of faces. Our results show that MCMCP requires fewer trials to obtain a higher quality estimate of people’s mental representations of these two categories. (shrink)

With small to modest sample sizes and complex models, maximum likelihood estimation of confirmatory factor analysis models can show serious estimation problems such as non-convergence or parameter estimates outside the admissible parameter space. In this article, we distinguish different Bayesian estimators that can be used to stabilize the parameter estimates of a CFA: the mode of the joint posterior distribution that is obtained from penalized maximum likelihood estimation, and the mean, median, or mode of the marginal posterior distribution that are (...) calculated by using Markov Chain Monte Carlo methods. In two simulation studies, we evaluated the performance of the Bayesian estimators from a frequentist point of view. The results show that the EAP produced more accurate estimates of the latent correlation in many conditions and outperformed the other Bayesian estimators in terms of root mean squared error. We also argue that it is often advantageous to choose a parameterization in which the main parameters of interest are bounded, and we suggest the four-parameter beta distribution as a prior distribution for loadings and correlations. Using simulated data, we show that selecting weakly informative four-parameter beta priors can further stabilize parameter estimates, even in cases when the priors were mildly misspecified. Finally, we derive recommendations and propose directions for further research. (shrink)

Role-based database management systems are most widely used for information storage and analysis but are known as vulnerable to insider attacks. The core of intrusion detection lies in an adaptive system, where an insider attack can be judged if it is different from the predicted role by performing classification on the user’s queries accessing the database and comparing it with the authorized role. In order to handle the high similarity of user queries for misclassified roles, this paper proposes a deep (...) metric neural network with strategic sampling algorithm that properly extracts salient features and directly learns a quantitative measure of similarity. A strategic sampling method of heuristically generating and learning training pairs through Monte Carlo search is proposed to select a training pair that can represent the entire dataset. With the TPC-E–based benchmark data trained with 11,000 queries for 11 roles, the proposed model produces the classification accuracy of 95.41%, which is the highest compared with the previous models. The results are verified through comparison of quantitative and qualitative evaluations, and the feature space modelled in the neural network is analysed by t-SNE algorithm. (shrink)

Drought is a creeping phenomenon that slowly holds an area over time and can be continued for many years. The impacts of drought occurrences can affect communities and environments worldwide in several ways. Thus, assessment and monitoring of drought occurrences in a region are crucial for reducing its vulnerability to the negative impacts of drought. Therefore, comprehensive drought assessment techniques and methods are required to develop adaptive strategies that a region can undertake to reduce its vulnerability to drought substantially. For (...) this purpose, this study proposes a new method known as a regional comprehensive assessment of meteorological drought. The Standardized Precipitation Index, Standardized Precipitation Evapotranspiration Index, and Standardized Precipitation and Temperature Index are jointly used for the development of the RCAMD. Further, the RCAMD employs Monte Carlo feature selection and steady-state probabilities to comprehensively collect information from various stations and drought indices. Moreover, the RCAMD is validated on the six selected stations in the northern areas of Pakistan. The outcomes associated with the RCAMD provide a comprehensive regional assessment of meteorological drought and become the initial source for bringing more considerations to drought monitoring and early warning systems. (shrink)

This work compares Autometrics with dual penalization techniques such as minimax concave penalty and smoothly clipped absolute deviation under asymmetric error distributions such as exponential, gamma, and Frechet with varying sample sizes as well as predictors. Comprehensive simulations, based on a wide variety of scenarios, reveal that the methods considered show improved performance for increased sample size. In the case of low multicollinearity, these methods show good performance in terms of potency, but in gauge, shrinkage methods collapse, and higher gauge (...) leads to overspecification of the models. High levels of multicollinearity adversely affect the performance of Autometrics. In contrast, shrinkage methods are robust in presence of high multicollinearity in terms of potency, but they tend to select a massive set of irrelevant variables. Moreover, we find that expanding the data mitigates the adverse impact of high multicollinearity on Autometrics rapidly and gradually corrects the gauge of shrinkage methods. For empirical application, we take the gold prices data spanning from 1981 to 2020. While comparing the forecasting performance of all selected methods, we divide the data into two parts: data over 1981–2010 are taken as training data, and those over 2011–2020 are used as testing data. All methods are trained for the training data and then are assessed for performance through the testing data. Based on a root-mean-square error and mean absolute error, Autometrics remain the best in capturing the gold prices trend and producing better forecasts than MCP and SCAD. (shrink)

Fodor argued that learning a concept by hypothesis testing would involve an impossible circularity. I show that Fodor's argument implicitly relies on the assumption that actually φ-ing entails an ability to φ. But this assumption is false in cases of φ-ing by luck, and just such luck is involved in testing hypotheses with the kinds of generative random sampling methods that many cognitive scientists take our minds to use. Concepts thus can be learned by hypothesis testing without circularity, and it (...) is plausible that this is how humans in fact acquire at least some of their concepts. (shrink)

Computational methods such as computer simulations, Monte Carlo methods, and agent-based modeling have become the dominant techniques in many areas of science. Extending Ourselves contains the first systematic philosophical account of these new methods, and how they require a different approach to scientific method. Paul Humphreys draws a parallel between the ways in which such computational methods have enhanced our abilities to mathematically model the world, and the more familiar ways in which scientific instruments have expanded our (...) access to the empirical world. This expansion forms the basis for a new kind of empiricism, better suited to the needs of science than the older anthropocentric forms of empiricism. Human abilities are no longer the ultimate standard of epistemological correctness.Humphreys also includes arguments for the primacy of properties rather than objects, the need to consider technological constraints when appraising scientific methods, and a detailed account of how the path from computational template to scientific application is constructed. This last feature allows us to hold a form of selective realism in which anti-realist arguments based on formal reconstructions of theories can be avoided. One important consequence of the rise of computational methods is that the traditional organization of the sciences is being replaced by an organization founded on computational templates.Extending Ourselves will be of interest to philosophers of science, epistemologists, and to anyone interested in the role played by computers in modern science. (shrink)

A SEM-based approach using likelihood-based confidence interval has been proposed to form confidence intervals for unstandardized and standardized indirect effect in mediation models. However, when used with the maximum likelihood estimation, this approach requires that the variables are multivariate normally distributed. This can affect the LBCIs of unstandardized and standardized effect differently. In the present study, the robustness of this approach when the predictor is not normally distributed but the error terms are conditionally normal, which does not violate the distributional (...) assumption of ordinary least squares estimation, is compared to four other approaches: nonparametric bootstrapping, two variants of LBCI, LBCI assuming the predictor is fixed and LBCI based on ADF estimation, and Monte Carlo. A simulation study was conducted using a simple mediation model and a serial mediation model, manipulating the distribution of the predictor. The Monte Carlo method performed worst among the methods. LBCI and LBCI-Fixed-X had suboptimal performance when the distributions had high kurtosis and the population indirect effects were medium to large. In some conditions, the problem was severe even when the sample size was large. LBCI-ADF and nonparametric bootstrapping had coverage probabilities close to the nominal value in nearly all conditions, although the coverage probabilities were still suboptimal for the serial mediation model when the sample size was small with respect to the model. Implications of these findings in the context of this special case of nonnormal data were discussed. (shrink)

Econometric Theory and Methods International Edition provides a unified treatment of modern econometric theory and practical econometric methods. The geometrical approach to least squares is emphasized, as is the method of moments, which is used to motivate a wide variety of estimators and tests. Simulation methods, including the bootstrap, are introduced early and used extensively. The book deals with a large number of modern topics. In addition to bootstrap and Monte Carlo tests, these include sandwich covariance matrix (...) estimators, artificial regressions, estimating functions and the generalized method of moments, indirect inference, and kernel estimation. Every chapter incorporates numerous exercises, some theoretical, some empirical, and many involving simulation. (shrink)

Multitrait-multimethod (MTMM) analysis is one of the most frequently employed methods to examine the validity of psychological measures. Confirmatory factor analysis (CFA) is a commonly used analytic tool for examining MTMM data through the specification of trait and method latent variables. Most contemporary CFA-MTMM models either do not allow estimating correlations between the trait and method factors or they are restricted to linear trait-method relationships. There is no theoretical reason why trait and method relationships should always (...) be linear, and quadratic relationships are frequently proposed in the social sciences. In this article, we present two approaches for examining quadratic relations between traits and methods through extended latent difference and latent means CFA-MTMM models (Pohl et al., 2008; Pohl and Steyer, 2010). An application of the new approaches to a multi-rater study of the nine inattention symptoms of attention-deficit/hyperactivity disorder in children (N = 752) and the results of a Monte Carlo study to test the applicability of the models under a variety of data conditions are described. (shrink)

Si le hasard est, dès 1913, un acteur à part entière de la production de Duchamp, il semble difficile d’en parler de manière générale, indépendamment des mécaniques ingénieuses inventées par l’auteur pour le « mettre en conserve ». Car le hasard, loin d’être un symbole de la chance ou de l’irrationnel, est avant tout une opération de pensée, qui se réinvente dans chaque œuvre à travers des méthodes différentes. Ainsi l’ Erratum musical permet-il de questionner la notion d’empreinte mémorielle, les (...) formes aléatoires des 3 stoppages étalon érodent la norme du mètre par excès de précision, et l’ Obligation pour la roulette de Monte-Carlo transforme la martingale en jeu de patience. En portant une attention précise aux opérations de hasard spécifiques à chaque œuvre ainsi qu’aux mouvements de pensée qui les accompagnent, nous verrons comment ces objets se font écho en élaborant, chacun à leur manière, une esthétique suspensive. (shrink)

The study of cultural evolution has taken on an increasingly interdisciplinary and diverse approach in explicating phenomena of cultural transmission and adoptions. Inspired by this computational movement, this study uses Bayesian networks analysis, combining both the frequentist and the Hamiltonian Markov chain Monte Carlo (MCMC) approach, to investigate the highly representative elements in the cultural evolution of a Vietnamese city’s architecture in the early 20th century. With a focus on the façade design of 68 old houses in Hanoi’s (...) Old Quarter (based on 78 data lines extracted from 248 photos), the study argues that it is plausible to look at the aesthetics, architecture, and designs of the house façade to find traces of cultural evolution in Vietnam, which went through more than six decades of French colonization and centuries of sociocultural influence from China. The in-depth technical analysis, though refuting the presumed model on the probabilistic dependency among the variables, yields several results, the most notable of which is the strong influence of Buddhism over the decorations of the house façade. Particularly, in the top 5 networks with the best Bayesian Information Criterion (BIC) scores and p<0.05, the variable for decorations (DC) always has a direct probabilistic dependency on the variable B for Buddhism. The paper then checks the robustness of these models using Hamiltonian MCMC method and find the posterior distributions of the models’ coefficients all satisfy the technical requirement. Finally, this study suggests integrating Bayesian statistics in the social sciences in general and for the study of cultural evolution and architectural transformation in particular. (shrink)

This paper introduces the new novel four-parameter Weibull distribution named as the Marshall–Olkin alpha power Weibull distribution. Some statistical properties of the distribution are examined. Based on Type-I censored and Type-II censored samples, maximum likelihood estimation, maximum product spacing, and Bayesian estimation for the MOAPW distribution parameters are discussed. Numerical analysis using real data sets and Monte Carlo simulation are accomplished to compare various estimation methods. This novel model’s supremacy upon some famous distributions is explained using two real (...) data sets and it is shown that the MOAPW model can achieve better fits than other competitive distributions. (shrink)

Background: Mental healthcare needs person-tailored interventions. Experience Sampling Method (ESM) can provide daily life monitoring of personal experiences. This study aims to operationalize and test a measure of momentary reward-related Quality of Life (rQoL). Intuitively, quality of life improves by spending more time on rewarding experiences. ESM clinical interventions can use this information to coach patients to find a realistic, optimal balance of positive experiences (maximize reward) in daily life. rQoL combines the frequency of engaging in a relevant context (...) (a ‘behavior setting’) with concurrent (positive) affect. High rQoL occurs when the most frequent behavior settings are combined with positive affect or infrequent behavior settings co-occur with low positive affect. Methods: Resampling procedures (Monte Carlo experiments) were applied to assess the reliability of rQoL using various behavior setting definitions under different sampling circumstances, for real or virtual subjects with low-, average- and high contextual variability. Furthermore, resampling was used to assess whether rQoL is a distinct concept from positive affect. Virtual ESM beep datasets were extracted from 1058 valid ESM observations for virtual and real subjects. Results: Behavior settings defined by Who-What contextual information were most informative. Simulations of at least 100 ESM observations are needed for reliable assessment. Virtual ESM beep datasets of a real subject can be defined by Who-What-Where behavior setting combinations. Large sample sizes are necessary for reliable rQoL assessments, except for subjects with low contextual variability. rQoL is distinct from positive affect. Conclusion: rQoL is a feasible concept. Monte Carlo experiments should be used to assess the reliable implementation of an ESM statistic. Future research in ESM should asses the behavior of summary statistics under different sampling situations. This exploration is especially relevant in clinical implementation, where often only small datasets are available. (shrink)

An increasing number of studies have addressed the psychological impact of the COVID-19 crisis on the general population. Nevertheless, far less is known about the impact on specific populations such as university students, whose psychological vulnerability has been shown in previous research. This study sought to examine different indicators of mental health in university students during the Spanish lockdown; we also analyzed the main sources of stress perceived by students in relation to the COVID-19 crisis, and the coping strategies adopted (...) when faced with the situation. Data was collected from 932 students through a web-based platform. Measures of anxiety, depression, irritability, and self-perceived change in mental health were administered, as well as ad hoc measures of stressors and coping strategies. Results indicated that students experienced considerable psychological problems during the confinement, with higher rates of emotional difficulties in women and undergraduate students than in men and postgraduates, respectively. Psychological distress was mainly related to several specific domains of stressors, as perceived by the participants: academic future, task overload, worsening of interpersonal conflicts, and restrictions in pleasant social contact; and far less related to the spread of the disease and its consequences for physical health. As regards coping strategies, both reframing skills and daily routines were shown to be the most effective. A path-analysis model integrating stressors, coping, and mental health revealed that coping strategies partially mediated the effect of stressors on psychological health. In general, results suggest that students’ psychological health was substantially affected by the COVID-19 situation and that the academic and relational changes were the most notable sources of stress. This study reinforces the need to monitor and promote mental health in university students to boost resilience in times of crisis. Our results on effective coping strategies may inform preventive programs aimed at helping students to deal with challenges like the COVID-19 pandemic. (shrink)

Models of category learning have been extensively studied in cognitive science and primarily tested on perceptual abstractions or artificial stimuli. In this paper, we focus on categories acquired from natural language stimuli, that is, words. We present a Bayesian model that, unlike previous work, learns both categories and their features in a single process. We model category induction as two interrelated subproblems: the acquisition of features that discriminate among categories, and the grouping of concepts into categories based on those features. (...) Our model learns categories incrementally using particle filters, a sequential Monte Carlo method commonly used for approximate probabilistic inference that sequentially integrates newly observed data and can be viewed as a plausible mechanism for human learning. Experimental results show that our incremental learner obtains meaningful categories which yield a closer fit to behavioral data compared to related models while at the same time acquiring features which characterize the learned categories. (shrink)

Most current statistical natural language processing models use only local features so as to permit dynamic programming in inference, but this makes them unable to fully account for the long distance structure that is prevalent in language use. We show how to solve this dilemma with Gibbs sam- pling, a simple Monte Carlo method used to perform approximate inference in factored probabilistic models. By using simulated annealing in place of Viterbi decoding in sequence models such as HMMs, (...) CMMs, and CRFs, it is possible to incorporate non-local structure while preserving tractable inference. We use this technique to augment an existing CRF-based information extraction system with long-distance dependency models, enforcing label consistency and extraction template consistency constraints. This technique results in an error reduction of up to 9% over state-of-the-art systems on two established information extraction tasks. (shrink)

Maximum likelihood (ML) estimation of categorical multitrait-multimethod (MTMM) data is challenging because the likelihood involves high-dimensional integrals over the crossed method and trait factors, with no known closed-form solution. The purpose of the study is to introduce three newly developed ML methods that are eligible for estimating MTMM models with categorical responses: Variational maximization-maximization (e.g., Rijmen and Jeon, 2013 ), alternating imputation posterior (e.g., Cho and Rabe-Hesketh, 2011 ), and Monte Carlo local likelihood (e.g., Jeon et al., (...) under revision). Each method is briefly described and its applicability for MTMM models with categorical data are discussed. (shrink)

This research aims to evaluate the calculation accuracy and efficiency of the artificial neural network-based important sampling method on reliability of structures such as drum brakes. The finite element analysis result is used to establish the ANN sample in ANN-based reliability analysis methods. Because the process of FEA is time-consuming, the ANN sample size has a very important influence on the calculation efficiency. Two types of ANNs used in this study are the radial basis function neural network and back (...) propagation neural network. RBF-IS and BP-IS methods are used to conduct reliability analysis on training samples of three different sizes, and the results are compared with several reliability analysis methods based on ANNs. The results show that the probability of failure of the RBF-IS method is closer to that of the Monte-Carlo simulation method than those of other methods. In addition, the RBF-IS method has better calculation efficiency than the other methods considered in this study. This research demonstrates that the RBF-IS method is well suited to structure reliability problems. (shrink)

This study investigated the impact of three prior distributions: matched, standard vague, and hierarchical in Bayesian estimation parameter recovery in two and one parameter models. Two Bayesian estimation methods were utilized: Markov chain Monte Carlo (MCMC) and the relatively new, Variational Bayesian (VB). Conditional (CML) and Marginal Maximum Likelihood (MML) estimates were used as baseline methods for comparison. Vague priors produced large errors or convergence issues and are not recommended. For both MCMC and VB, the hierarchical and matched (...) priors showed the lowest root mean squared errors (RMSEs) for ability estimates; RMSEs of difficulty estimates were similar across estimation methods. For the standard errors (SEs), MCMC-hierarchical displayed the largest values across most conditions. SEs from the VB estimation were among the lowest in all but one case. Overall, VB-hierarchical, VB-matched, and MCMC-matched performed best. VB with hierarchical priors are recommended in terms of their accuracy, and cost and (subsequently) time effectiveness. (shrink)

Algorithms for approximate Bayesian inference, such as those based on sampling (i.e., Monte Carlo methods), provide a natural source of models of how people may deal with uncertainty with limited cognitive resources. Here, we consider the idea that individual differences in working memory capacity (WMC) may be usefully modeled in terms of the number of samples, or “particles,” available to perform inference. To test this idea, we focus on two recent experiments that report positive associations between WMC and (...) two distinct aspects of categorization performance: the ability to learn novel categories, and the ability to switch between different categorization strategies (“knowledge restructuring”). In favor of the idea of modeling WMC as a number of particles, we show that a single model can reproduce both experimental results by varying the number of particles—increasing the number of particles leads to both faster category learning and improved strategy‐switching. Furthermore, when we fit the model to individual participants, we found a positive association between WMC and best‐fit number of particles for strategy switching. However, no association between WMC and best‐fit number of particles was found for category learning. These results are discussed in the context of the general challenge of disentangling the contributions of different potential sources of behavioral variability. (shrink)

Articulated arm coordinate measuring machine is a specific robotic structural instrument, which uses D-H method for the purpose of kinematic modeling and error compensation. However, it is difficult for the existing error compensation models to describe various factors, which affects the accuracy of AACMM. In this paper, a modeling and error compensation method for AACMM is proposed based on BP Neural Networks. According to the available measurements, the poses of the AACMM are used as the input, and the (...) coordinates of the probe are used as the output of neural network. To avoid tedious training and improve the training efficiency and prediction accuracy, a data acquisition strategy is developed according to the actual measurement behavior in the joint space. A neural network model is proposed and analyzed by using the data generated via Monte-Carlo method in simulations. The structure and parameter settings of neural network are optimized to improve the prediction accuracy and training speed. Experimental studies have been conducted to verify the proposed algorithm with neural network compensation, which shows that 97% error of the AACMM can be eliminated after compensation. These experimental results have revealed the effectiveness of the proposed modeling and compensation method for AACMM. (shrink)

The procedures of validating computer simulations of particle physicsParticle physics events at the LHCLarge Hadron Collider are summarized. Because of the strongly fluctuating particle content of LHC events and detectorDetector interactions, particle-based Monte Carlo methods are an indispensable tool for dataData analysis analysis. Simulation in particle physicsParticle physics is founded on factorizationFactorization and thus its global validation can be realized by validating each individual step in the simulation. This can be accomplished by drawing on results of previousMeasurement measurements, (...) in situ studies, and models. What is particularly important in particle physicsParticle physics is to quantify how well a simulation is validated such that a systematic uncertainty can be assigned to a measurement. The simulation is tested for a wide range of processes and agrees with dataData within the assigned uncertainties. (shrink)

In consideration of that the correlation between any two assets of the asset pool is always stochastic in the actual market and that collateralized debt obligation pricing models under nonhomogeneous assumptions have no semianalytic solutions, we designed a numerical algorithm based on randomized quasi-Monte Carlo simulation method for CDO pricing with stochastic correlations under nonhomogeneous assumptions and took Gaussian factor copula model as an example to conduct experiments. The simulation results of RQMC and Monte Carlo (...) method were compared from the perspective of variance changes. The results showed that this numerical algorithm was feasible, efficient, and stable for CDO pricing with stochastic correlation under nonhomogeneous assumptions. This numerical algorithm is expected to be extended to other factor Copula models for CDO pricing with stochastic correlations under nonhomogeneous assumptions. (shrink)

What is the significance of high-speed computation for the sciences? How far does it result in a practice of simulation which affects the sciences on a very basic level? To offer more historical context to these recurring questions, this paper revisits the roots of computer simulation in the development of the ENIAC computer and the Monte Carlo method. With the aim of identifying more clearly what really changed (or not) in the history of science in the 1940s (...) and 1950s due to the computer, I will emphasize the continuities with older practices and develop a two-fold argument. Firstly, one can find a diversity of practices around ENIAC which tends to be ignored if one focuses only on the ENIAC itself as the originator of Monte Carlo simulation. Following from this, I claim, secondly, that there was no simulation around ENIAC. Not only is the term ‘simulation’ not used within that context, but the analysis also shows how ‘simulation’ is an effect of three interrelated sets of different practices around the machine: (1) the mathematics which the ENIAC users employed and developed, (2) the programs, (3) the physicality of the machine. I conclude that, in the context discussed, the most important shifts in practice are about rethinking existing computational methods. This was done in view of adapting them to the high-speed and programmability of the new machine. Simulation then is but one facet of this process of adaptation, singled out by posterity to be viewed as its principal aspect. (shrink)

What is the significance of high-speed computation for the sciences? How far does it result in a practice of simulation which affects the sciences on a very basic level? To offer more historical context to these recurring questions, this paper revisits the roots of computer simulation in the development of the ENIAC computer and the Monte Carlo method.With the aim of identifying more clearly what really changed (or not) in the history of science in the 1940s and (...) 1950s due to the computer, I will emphasize the continuities with older practices and develop a two-fold argument. Firstly, one can find a diversity of practices around ENIAC which tends to be ignored if one focuses only on the ENIAC itself as the originator of Monte Carlo simulation. Following from this, I claim, secondly, that there was no simulation around ENIAC. Not only is the term ‘simulation’ not used within that context, but the analysis also shows how ‘simulation’ is an effect of three interrelated sets of different practices around the machine: (1) the mathematics which the ENIAC users employed and developed, (2) the programs, (3) the physicality of the machine. I conclude that, in the context discussed, the most important shifts in practice are about rethinking existing computational methods. This was done in view of adapting them to the high-speed and programmability of the new machine. Simulation then is but one facet of this process of adaptation, singled out by posterity to be viewed as its principal aspect. (shrink)

The self-avoiding walk is a classical model in statistical mechanics, probability theory and mathematical physics. It is also a simple model of polymer entropy which is useful in modelling phase behaviour in polymers. This monograph provides an authoritative examination of interacting self-avoiding walks, presenting aspects of the thermodynamic limit, phase behaviour, scaling and critical exponents for lattice polygons, lattice animals and surfaces. It also includes a comprehensive account of constructive methods in models of adsorbing, collapsing, and pulled walks, animals and (...) networks, and for models of walks in confined geometries. Additional topics include scaling, knotting in lattice polygons, generating function methods for directed models of walks and polygons, and an introduction to the Edwards model.This essential second edition includes recent breakthroughs in the field, as well as maintaining the older but still relevant topics. New chapters include an expanded presentation of directed models, an exploration of methods and results for the hexagonal lattice, and a chapter devoted to the Monte Carlo methods. (shrink)

In several experiments of survival analysis, the cause of death or failure of any subject may be characterized by more than one cause. Since the cause of failure may be dependent or independent, in this work, we discuss the competing risk lifetime model under progressive type-II censored where the removal follows a binomial distribution. We consider the Akshaya lifetime failure model under independent causes and the number of subjects removed at every failure time when the removal follows the binomial distribution (...) with known parameters. The classical and Bayesian approaches are used to account for the point and interval estimation procedures for parameters and parametric functions. The Bayes estimate is obtained by using the Markov Chain Monte Carlo method under symmetric and asymmetric loss functions. We apply the Metropolis–Hasting algorithm to generate MCMC samples from the posterior density function. A simulated data set is applied to diagnose the performance of the two techniques applied here. The data represented the survival times of mice kept in a conventional germ-free environment, all of which were exposed to a fixed dose of radiation at the age of 5 to 6 weeks, which was used as a practice for the model discussed. There are 3 causes of death. In group 1, we considered thymic lymphoma to be the first cause and other causes to be the second. On the base of mice data, the survival mean time of mice of the second cause is higher than the first cause. (shrink)

Cooperative spreading dynamics on complex networks is a hot topic in the field of network science. In this paper, we propose a strategy to immunize some nodes based on their degrees. The immunized nodes disable the synergistic effect of cooperative spreading dynamics. We also develop a generalized percolation theory to study the final state of the spreading dynamics. By using the Monte Carlo method, numerical simulations reveal that immunizing nodes with a large degree cannot always be beneficial (...) for containing cooperative spreading. For small values of transmission probability, immunizing hubs can suppress the spreading, while the opposite situation happens for large values of transmission probability. Furthermore, numerical simulations show that immunizing hubs increase the cost of the system. Finally, all numerical simulations can be well predicted by the generalized percolation theory. (shrink)

Happiness is the eternal pursuit of mankind and is also the ultimate goal of social governance and national development. Based on data from the Chinese General Social Survey, this study used a structural equation model to analyze the influence of social class identity and public service satisfaction on the happiness of residents. The effect of public service satisfaction and social trust between social class identity and residents’ happiness was tested using the Monte Carlo method. The empirical results (...) show that social class identity, social trust, and public service satisfaction all had a significant positive impact on residents’ happiness. The influence coefficients of social class identity on the happiness of residents and on the satisfaction degree of public service for those born in the1970s group were greater than the 1980s and 1990s groups. The influence coefficients of social class identity on the happiness and public service satisfaction of non-agricultural household residents were greater than those of agricultural household residents. Therefore, to improve the happiness of residents, we should make new breakthroughs in the equality of quality and the quality of public services, promote the integration of urban and rural areas, highlight key areas of rural development, and increase the construction of basic public services for agricultural household residents. (shrink)

This paper is concerned with the estimation of the Weibull generalized exponential distribution parameters based on the adaptive Type-II progressive censored sample. Maximum likelihood estimation, maximum product spacing, and Bayesian estimation based on Markov chain Monte Carlo methods have been determined to find the best estimation method. The Monte Carlo simulation is used to compare the three methods of estimation based on the ATIIP-censored sample, and also, we made a bootstrap confidence interval estimation. We will (...) analyze data related to the distribution about single carbon fiber and electrical data as real data cases to show how the schemes work in practice. (shrink)

Ancient algorithms -- Sorting -- Graph algorithms -- Primality testing -- Solution of linear equations -- Newton's method -- Linear programming -- Eigenvalues and eigenvectors -- MM algorithms -- Data mining -- The fast Fourier transform -- Monte Carlo methods -- Mathematical review.

The aim of cognitive diagnosis is to classify respondents' mastery status of latent attributes from their responses on multiple items. Since respondents may answer some but not all items, item-level missing data often occur. Even if the primary interest is to provide diagnostic classification of respondents, misspecification of missing data mechanism may lead to biased conclusions. This paper proposes a joint cognitive diagnosis modeling of item responses and item-level missing data mechanism. A Bayesian Markov chain Monte Carlo (...) class='Hi'>method is developed for model parameter estimation. Our simulation studies examine the parameter recovery under different missing data mechanisms. The parameters could be recovered well with correct use of missing data mechanism for model fit, and missing that is not at random is less sensitive to incorrect use. The Program for International Student Assessment 2015 computer-based mathematics data are applied to demonstrate the practical value of the proposed method. (shrink)

The risk priority number calculation method is one of the critical subjects of failure mode and effects analysis research. Recently, RPN research under a fuzzy uncertainty environment has become a hot topic. Accordingly, increasing studies have ignored the important impact of the random sampling uncertainty in the FMEA assessment. In this study, a fuzzy beta-binomial RPN evaluation method is proposed by integrating fuzzy theory, Bayesian statistical inference, and the beta-binomial distribution. This model can effectively realize real-time, dynamic, and (...) long-term evaluation of RPN under the condition of continuous knowledge accumulation. The major contribution of the proposed model is to use the random uncertainty and fuzzy uncertainty in an integrated model and provide a Markov Chain Monte Carlo method to solve the complex integrated model. The study presented a case study, which presented how to apply this model in practice and indicated the significant influence on the measurement error caused by ignoring the random uncertainty caused by expert evaluation in RPN calculations. (shrink)

Monte Carlo simulations arrive at their results by introducing randomness, sometimes derived from a physical randomizing device. Nonetheless, we argue, they open no new epistemic channels beyond that already employed by traditional simulations: the inference by ordinary argumentation of conclusions from assumptions built into the simulations. We show that Monte Carlo simulations cannot produce knowledge other than by inference, and that they resemble other computer simulations in the manner in which they derive their conclusions. Simple examples (...) of Monte Carlo simulations are analysed to identify the underlying inferences. (shrink)

The exponential growth of social data both in volume and complexity has increasingly exposed many of the shortcomings of the conventional frequentist approach to statistics. The scientific community has called for careful usage of the approach and its inference. Meanwhile, the alternative method, Bayesian statistics, still faces considerable barriers toward a more widespread application. The bayesvl R package is an open program, designed for implementing Bayesian modeling and analysis using the Stan language’s no-U-turn (NUTS) sampler. The package combines the (...) ability to construct Bayesian network models using directed acyclic graphs (DAGs), the Markov chain Monte Carlo (MCMC) simulation technique, and the graphic capability of the ggplot2 package. As a result, it can improve the user experience and intuitive understanding when constructing and analyzing Bayesian network models. A case example is offered to illustrate the usefulness of the package for Big Data analytics and cognitive computing. (shrink)

In recent years, computational sciences such as computational hydrodynamics or computational field theory have supplemented theoretical and experimental investigations in many scientific fields. Often, there is a seemingly fruitful overlap between theory, experiment, and numerics. The computational sciences are highly dynamic and seem a fairly successful endeavor---at least if success is measured in terms of publications or engineering applications. However, for theories, success in application and correctness are two very different things; and just the same may hold for "methodologies" like (...) computer simulations. A lively debate on the epistemic status of computer simulations has thus emerged within the philosophy of science. This paper discusses possible problems when computer simulation and laboratory experiment are intertwined. In present experiments, stochastic methods in the form of Monte Carlo simulations are often involved in generating experimental data. It is questioned as to how far a realistic stance can be maintained when such stochastic elements are involved. Taking experiments in high energy physics as a study case, this paper contends that using these types of entangled material and numerical experiments as a source of new phenomena or for theory testing must presuppose a certain understanding of causality and thus binds us at least to a weak form of realism. (shrink)