To begin our discussion, we will look at some basic ideas of probability; in particular, the idea of how the behavior of a system can be described by a curve called the probability density function, step 2.1: save, on a histogram, the values of M and M2. Nat Rev Phys 5, 372 (2023). traditional MC faces questions of transient statistical consistency . Springer-Verlag, New York, Robert C, Casella G (2010) Introducing Monte Carlo methods withR. Springer, New York, Rosenthal J (2007) AMCM: an R interface for adaptive MCMC. M {\displaystyle viz_{i}} 1 This book provides an introduction to Monte Carlo simulations in classical statistical physics and is aimed both at students beginning work in the field and at more experienced researchers who wish to learn more about Monte Carlo methods. On the other hand, AMC which is adopted here, addresses these issues on-the-fly using defined bounds on estimation accuracy as well as ensemble enrichment routines. 32 (6), August, 2005), "This revision of the influential 1999 text includes changes to the presentation in the early chapters and much new material related to MCMC and Gibbs sampling. . p He is a fellow of the Institute of Mathematical Statistics, and a winner of the Young Statistician Award of the Societi de Statistique de Paris in 1995. {\displaystyle A_{\vec {r}}^{*}} Monte Carlo methods are a class of techniques for randomly sampling a probability distribution. {\displaystyle M=M+\Delta M}. The purpose of this paper is to describe a general method, suitable for fast electronic computing machines, of calculating the properties of any substance which may be considered as composed of interacting individual molecules. This is, in a nutshell, a 1953 article by Nicholas Metropolis, Arianna and Marshall Rosenbluth and Augusta and Edward Teller. 1 The central limit theorem tells us that the distribution of the errors will converge to a normal distribution and with this notion in mind, we can figure out the number of times we need to resample to achieve a certain accuracy. Please try again. 1096 (22), 2006), "This is a useful and utilitarian book. Monte Carlo techniques towards their use in Statis-tics, referring to Robert and Casella (2004, 2010) for an in-depth coverage. the book is also very well suited for self-study and is also a valuable reference for any statistician who wants to study and apply these techniques." 32 (6), August, 2005), "This revision of the influential 1999 text includes changes to the presentation in the early chapters and much new material related to MCMC and Gibbs sampling. The final two chapters of the book cover methods that, when compared with the material in the first 12 chapters, are still in their beginning stages of development. {\displaystyle \beta =1/k_{b}T} Still, the computational efficiency of numerous routines within the AMC framework have yet to be addressed, leading to the first pillar of this dissertation. The numerous problems include many with analytical components. Am. A Monte Carlo simulation with 10,000 iterations and a cohort size of 10,000 was employed to evaluate the cost-utility from a societal perspective. Monte Carlo Simulation Methods I Computational tools for thesimulation of random variablesand the approximation of integrals/expectations. E Abstract The Bayesian approach allows an intuitive way to derive the methods of statistics. The researcher then performs the multiplication of that value by the integral (b-a) in order to obtain the integral. J. r A A 1431-875X, Series E-ISSN: In these, the parameter space is not well defined, being a collection of unrelated subspaces, and requires more advanced tools, namely variable dimension models, which are presented in chapter 11. George Casella is Distinguished Professor and Chair, Department of Statistics, University of Florida. spins, and so, the phase space is discrete and is characterized by N spins, He also edited Discretization and MCMC Convergence Assessment, Springer 1998. The result is a useful introduction to Monte Carlo methods and a convenient reference for much of current methodology. That is, because AMC is capable of bounding errors in uncertainty quantification with respect to a quantity of interest, it can be utilized in a closed-loop architecture to guide the model improvement process. One of the Monte Carlo methods is a crude Monte Carlo method. Accessibility StatementFor more information contact us atinfo@libretexts.org. Monte Carlo experimentation is the use of simulated random numbers to estimate some functions of a probability distribution. ] Google Scholar, Department of Statistics, University of Florida, Gainesville, USA, New advances are covered in the second edition, Request lecturer material: sn.pub/lecturer-material, Part of the book series: Springer Texts in Statistics (STS), 2654 This is necessary, but nonetheless insufficient from an implementation point of view. Please download or close your previous search result export first before starting a new bulk export. The material covered includes methods for both equilibrium and out of equilibrium systems, and common algorithms like the Metropolis and heat-bath algorithms are discussed in detail, as well as more sophisticated ones such as continuous time Monte Carlo, cluster algorithms, multigrid methods, entropic sampling and simulated tempering. Specifically, this dissertation will study the problem of uncertainty quantification for complex dynamical systems in the framework of particle methods and address the effectiveness of the solution methodology known as adaptive Monte Carlo (AMC). It is important to adequately approximate the spectmm of the data series being investigated. At the end of the book the authors give a number of example programmes demonstrating the applications of these techniques to a variety of well-known models. One important issue must be considered when using the metropolis algorithm with the canonical distribution: when performing a given measure, i.e. This is a textbook intended for a second-year graduate course. E , to choose for the importance sampling is the Boltzmann distribution or canonic distribution. 1 It provides enough of a foundation to gain an understanding of the chapters that follow. b The chapter gives the results needed to establish the convergence of various Monte Carlo Markov chain algorithms, and, more generally, to understand the literature on this topic. Analysis using Monte Carlo methods in general, and Monte Carlo Markov chains specifically, is now part of the applied statistician's toolkit. Phys. Monte Carlo simulation works by selecting a random value for each task, and then building models based on those values. { The_Monte_Carlo_Simulation_Method : "property get [Map MindTouch.Deki.Logic.ExtensionProcessorQueryProvider+<>c__DisplayClass230_0.b__1]()", The_Monte_Carlo_Simulation_V2 : "property get [Map MindTouch.Deki.Logic.ExtensionProcessorQueryProvider+<>c__DisplayClass230_0.b__1]()", Understanding_the_Geometry_of_High_Dimensional_Data_through_Simulation : "property get [Map MindTouch.Deki.Logic.ExtensionProcessorQueryProvider+<>c__DisplayClass230_0.b__1]()" }, { "Book:_Linear_Regression_Using_R_-_An_Introduction_to_Data_Modeling_(Lilja)" : "property get [Map MindTouch.Deki.Logic.ExtensionProcessorQueryProvider+<>c__DisplayClass230_0.b__1]()", "RTG:_Classification_Methods" : "property get [Map MindTouch.Deki.Logic.ExtensionProcessorQueryProvider+<>c__DisplayClass230_0.b__1]()", "RTG:_Simulating_High_Dimensional_Data" : "property get [Map MindTouch.Deki.Logic.ExtensionProcessorQueryProvider+<>c__DisplayClass230_0.b__1]()", "Supplemental_Modules_(Computing_and_Modeling)" : "property get [Map MindTouch.Deki.Logic.ExtensionProcessorQueryProvider+<>c__DisplayClass230_0.b__1]()" }, https://stats.libretexts.org/@app/auth/3/login?returnto=https%3A%2F%2Fstats.libretexts.org%2FBookshelves%2FComputing_and_Modeling%2FRTG%253A_Simulating_High_Dimensional_Data%2FThe_Monte_Carlo_Simulation_V2, \( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}}}\) \( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash{#1}}} \)\(\newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\) \( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\) \( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\) \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\) \( \newcommand{\Span}{\mathrm{span}}\) \(\newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\) \( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\) \( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\) \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\) \( \newcommand{\Span}{\mathrm{span}}\)\(\newcommand{\AA}{\unicode[.8,0]{x212B}}\), Understanding the Geometry of High Dimensional Data through Simulation, Example 2 - Approximating Distribution of Sample Mean. Such a simulation is, in turn, based on the production of uniform random variables. One possible approach to solve this multivariable integral is to exactly enumerate all possible configurations of the system, and calculate averages at will. A one-semester course on random variable generation and Markov chain theory could be based on chapters 1 to 7. {\displaystyle p({\vec {r}})} This type of Monte Carlo method is used to solve the integral of a particular function, for example, f(x) under the limits a and b. In this type of Monte Carlo method, the researcher takes a number N of the random sample, s. In this type of Monte Carlo method, the range on which the function is being integrated (i.e. The Monte Carlo method uses a random sampling of information to solve a statistical problem; while a simulation is a way to virtually demonstrate a strategy. J Roy Stat Soc B 56:501514, Gelfand A, Smith A (1990) Sampling based approaches to calculating marginal densities. One of the vital uses of Monte Carlo methods involves the evaluation of the difficult integrals. The following steps are to be made to perform a single measurement. The system's energy is given by {\displaystyle \sigma _{i}} The result is a very useful resource for anyone wanting to understand Monte Carlo procedures. . Model the system by using an appropriate probability density function. In realistic systems, on the other hand, an exact enumeration can be difficult or impossible to implement. M This method is helpful for the researcher to obtain the variance by adding up the variances for each sub interval. 1 And here we have the classic textbook about it, now in its second edition. Your file of search results citations is now ready. Monte Carlo (MC) approach to analysis was developed in the 1940's, it is a computer based analytical method which employs statistical sampling techniques for obtaining a probabilistic. And while its scientific legacy is broadly appreciated, the behind the paper story is less known. International Encyclopedia of Statistical Science pp 854858Cite as. With a computer, we generate a sample of independent draws from the distribution of . Chapters 6 to 9 are totally concerned with Monte Carlo Markov chain methodology. ) {\displaystyle A} PubMed Poor inputs/model will lead to meaningless outputs. Note that with any simulation, the results are as good as the inputs you give in. This second edition is a considerably enlarged version of the first. r / 1 r Comput Stat Data Anal 51:54675470, Rubinstein R (1981) Simulation and the Monte Carlo method. {\displaystyle \alpha \in [0,1]} Google Scholar, Chen M, Shao Q, Ibrahim J (2000) Monte Carlo methods in Bayesian computation. and. . The ACM Digital Library is published by the Association for Computing Machinery. e One tunneling time is defined as the number of steps 1. the system needs to make to go from the minimum of its energy to the maximum of its energy and return. {\displaystyle \Omega (E)} 1 i the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in In the estimation of \( \pi \), we would expect that as we increase the amount of sand we drop onto the square, the closer we are to the value of \( \pi \). Unfortu nately, a few times throughout the book a somewhat more advanced no tion is needed. ADS {\displaystyle \langle M\rangle } Proceedings of the Genetic and Evolutionary Computation Conference Companion, (1860-1869), Mandrioli C and Maggio M Testing self-adaptive software with probabilistic guarantees on performance metrics Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, (1002-1014), Sadeghi M, Leglaive S, Alameda-Pineda X, Girin L and Horaud R, Costa G and Ortale R Collaborative Recommendation ofTemporally-Discounted Tag-Based Expertise for Community Question Answering Advances in Knowledge Discovery and Data Mining, (41-52), Tangkaratt V, Han B, Khan M and Sugiyama M Variational imitation learning with diverse-quality demonstrations Proceedings of the 37th International Conference on Machine Learning, (9407-9417), Vaton S, Brun O, Mouchet M, Belzarena P, Amigo I, Prabhu B and Chonavel T, Sekiguchi K, Bando Y, Nugraha A, Yoshii K and Kawahara T, Wigren A, Risuleo R, Murray L and Lindsten F Parameter elimination in particle Gibbs sampling Proceedings of the 33rd International Conference on Neural Information Processing Systems, (8918-8929), Liu S, Kanamori T, Jitkrittum W and Chen Y Fisher efficient inference of intractable models Proceedings of the 33rd International Conference on Neural Information Processing Systems, (8793-8803), Kanagawa M and Hennig P Convergence guarantees for adaptive Bayesian quadrature methods Proceedings of the 33rd International Conference on Neural Information Processing Systems, (6237-6248), Campbell T and Beronov B Sparse variational inference Proceedings of the 33rd International Conference on Neural Information Processing Systems, (11461-11472), Zhu M Sample adaptive MCMC Proceedings of the 33rd International Conference on Neural Information Processing Systems, (9066-9077), Gautier G, Bardenet R and Valko M On two ways to use determinantal point processes for Monte Carlo integration Proceedings of the 33rd International Conference on Neural Information Processing Systems, (7770-7779), Belhadji A, Bardenet R and Chainais P Kernel quadrature with DPPs Proceedings of the 33rd International Conference on Neural Information Processing Systems, (12927-12937), Diesendruck M, Elenberg E, Sen R, Cole G, Shakkottai S and Williamson S Importance Weighted Generative Networks Machine Learning and Knowledge Discovery in Databases, (249-265), Ai Q, Wang X, Bruch S, Golbandi N, Bendersky M and Najork M Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval, (85-92), Aubry A, Carotenuto V, De Maio A and Govoni M, Costilla R, Liu I, Arnold R and Fernndez D, Wang X, Li C, Golbandi N, Bendersky M and Najork M The LambdaLoss Framework for Ranking Metric Optimization Proceedings of the 27th ACM International Conference on Information and Knowledge Management, (1313-1322), Rowland M, Choromanski K, Chalus F, Pacchiano A, Sarls T, Turner R and Weller A Geometrically coupled monte carlo sampling Proceedings of the 32nd International Conference on Neural Information Processing Systems, (195-205), Lyddon S, Walker S and Holmes C Nonparametric learning from Bayesian models with randomized objective functions Proceedings of the 32nd International Conference on Neural Information Processing Systems, (2075-2085), Peherstorfer B, Gunzburger M and Willcox K, Plazolles B, El Baz D, Spel M, Rivola V and Gegout P, Lagrange A, Fauvel M, May S and Dobigeon N A Bayesian Model for Joint Unmixing and Robust Classification of Hyperspectral Images 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (3399-3403), Arneberg J, Tal E and Karaman S Guidance Laws for Partially-Observable Interception Based on Linear Covariance Analysis 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (4185-4191), Said S, Bombrun L, Berthoumieu Y and Manton J, Zhang C, R C, Cafarella M, De Sa C, Ratner A, Shin J, Wang F and Wu S, Dutta R, Schoengens M, Onnela J and Mira A ABCpy Proceedings of the Platform for Advanced Scientific Computing Conference, (1-9), Bogert K and Doshi P Scaling Expectation-Maximization for Inverse Reinforcement Learning to Multiple Robots under Occlusion Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, (522-529), Pires B, Goldstein J, Higdon D, Sabin P, Korkmaz G, Shipp S, Keller S, Ba S, Hamall K, Koehler A and Reese S A Bayesian simulation approach for supply chain synchronization Proceedings of the 2017 Winter Simulation Conference, (1-12), Dieng A, Tran D, Ranganath R, Paisley J and Blei D Variational inference via X upper bound minimization Proceedings of the 31st International Conference on Neural Information Processing Systems, (2729-2738), Kucukelbir A, Wang Y and Blei D Evaluating Bayesian models with posterior dispersion indices Proceedings of the 34th International Conference on Machine Learning - Volume 70, (1925-1934), Kucukelbir A, Tran D, Ranganath R, Gelman A and Blei D, Saeedi A, Kulkarni T, Mansinghka V and Gershman S, Sa C, Ratner A, R C, Shin J, Wang F, Wu S and Zhang C, Martino L, Elvira V, Luengo D and Corander J, Erener A, Sivas A, Selcuk-Kestel A and Dzgn H, Ruiz-Sarmiento J, Galindo C and Gonzalez-Jimenez J, Elvira V, Martino L, Luengo D and Bugallo M, Meja Y, Arguello H, Costa F, Tourneret J and Batatia H Bayesian reconstruction of hyperspectral images by using compressed sensing measurements and a local structured prior 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (3116-3120), Forti N, Battistelli G, Chisci L and Sinopoli B Worst-case analysis of joint attack detection and resilient state estimation 2017 IEEE 56th Annual Conference on Decision and Control (CDC), (182-188), Yang C and Kumar M On the transient performance of Monte Carlo simulations for initial uncertainty forecasting 2017 IEEE 56th Annual Conference on Decision and Control (CDC), (1669-1674), Myrvoll T, Hkegrd J, Matsui T and Septier F Counting public transport passenger using WiFi signatures of mobile devices 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), (1-6), Hammer H, yazidi A and Begnum K Reliable modeling of CPU usage in an office worker environment Proceedings of the 31st Annual ACM Symposium on Applied Computing, (480-483), Bi B and Cho J Modeling a Retweet Network via an Adaptive Bayesian Approach Proceedings of the 25th International Conference on World Wide Web, (459-469), De Sa C, Ratner A, R C, Shin J, Wang F, Wu S and Zhang C, Onken A and Panzeri S Mixed vine copulas as joint models of spike counts and local field potentials Proceedings of the 30th International Conference on Neural Information Processing Systems, (1333-1341), Zhang Y, Wang X, Chen C, Henao R, Fan K and Carin L Towards unifying Hamiltonian Monte Carlo and slice sampling Proceedings of the 30th International Conference on Neural Information Processing Systems, (1749-1757), Zhang Y, Chen C, Henao R and Carin L Laplacian Hamiltonian Monte Carlo European Conference on Machine Learning and Knowledge Discovery in Databases - Volume 9851, (98-114), Grana J, Wolpert D, Neil J, Xie D, Bhattacharya T and Bent R, Ruiz F, Titsias M and Blei D Overdispersed black-box variational inference Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, (647-656), Kolesov I, Lee J, Sharp G, Vela P and Tannenbaum A, Altmann Y, Ren X, McCarthy A, Buller G and McLaughlin S, Ala-Luhtala J, Whiteley N, Heine K and Pich R, Wang B and Klabjan D Temporal topic analysis with endogenous and exogenous processes Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, (3045-3051), Zhang C, Shin J, R C, Cafarella M and Niu F Extracting Databases from Dark Data with DeepDive Proceedings of the 2016 International Conference on Management of Data, (847-859), Wright J, Thornton C and Leyton-Brown K Mechanical TA Proceedings of the 46th ACM Technical Symposium on Computer Science Education, (96-101), Ahmed N, Campbell M, Casbeer D, Cao Y and Kingston D Fully bayesian learning and spatial reasoning with flexible human sensor networks Proceedings of the ACM/IEEE Sixth International Conference on Cyber-Physical Systems, (80-89), Segovia-Dominguez I and Hernandez-Aguirre A An Estimation of Distribution Algorithm based on the Natural Gradient and the Boltzmann Distribution Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, (527-534), Cao P, Badger E, Kalbarczyk Z, Iyer R and Slagell A Preemptive intrusion detection Proceedings of the 2015 Symposium and Bootcamp on the Science of Security, (1-12), Borges M, Filieri A, D'Amorim M and Psreanu C Iterative distribution-aware sampling for probabilistic symbolic execution Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering, (866-877), Boshmaf Y, Ripeanu M, Beznosov K and Santos-Neto E Thwarting Fake OSN Accounts by Predicting their Victims Proceedings of the 8th ACM Workshop on Artificial Intelligence and Security, (81-89), Shin J, Wu S, Wang F, De Sa C, Zhang C and R C, Dobler M, Harrant M, Rafaila M, Pelz G, Rosenstiel W and Bogdan M Bordersearch Proceedings of the 2015 Design, Automation & Test in Europe Conference & Exhibition, (1036-1041), Ahmadyan S, Gu C, Natarajan S, Chiprout E and Vasudevan S Fast eye diagram analysis for high-speed CMOS circuits Proceedings of the 2015 Design, Automation & Test in Europe Conference & Exhibition, (1377-1382), Gu F, Butt M, Ai C, Shen X and Xiao J Adaptive particle filtering in data assimilation of wildfire spread simulation Proceedings of the Conference on Summer Computer Simulation, (1-10), Gu F Adaptively perturbing localized state space in data assimilation of wildfire spread simulation Proceedings of the 48th Annual Simulation Symposium, (163-172), Kandasamy K, Schneider J and Pczos B Bayesian active learning for posterior estimation Proceedings of the 24th International Conference on Artificial Intelligence, (3605-3611), Dalchau N, Murphy N, Petersen R and Yordanov B Synthesizing and Tuning Chemical Reaction Networks with Specified Behaviours Proceedings of the 21st International Conference on DNA Computing and Molecular Programming - Volume 9211, (16-33), Masada T and Takasu A Traffic Speed Data Investigation with Hierarchical Modeling Proceedings of the Second International Conference on Future Data and Security Engineering - Volume 9446, (123-134), Mazet V, Faisan S, Awali S, Gaveau M and Poisson L, Lasserre M, Bidon S, Besson O and Le Chevalier F, Mahmood A and Sutton R Off-policy learning based on weighted importance sampling with linear computational complexity Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, (552-561), Polatkan G, Zhou M, Carin L, Blei D and Daubechies I, Archambeau C, Lakshminarayanan B and Bouchard G, Combrexelle S, Wendt H, Dobigeon N, Tourneret J, McLaughlin S and Abry P, Lei Xiao , Gregson J, Heide F and Heidrich W, Borges M, Filieri A, d'Amorim M, Psreanu C and Visser W Compositional solution space quantification for probabilistic software analysis Proceedings of the 35th ACM SIGPLAN Conference on Programming Language Design and Implementation, (123-132), Wang T, Wang D and Wang F Quantifying herding effects in crowd wisdom Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, (1087-1096), Borges M, Filieri A, d'Amorim M, Psreanu C and Visser W, Floriello D, Botterill T and Green R A topological constrained model-based approach to correspondence problems for branched structures Proceedings of the 29th International Conference on Image and Vision Computing New Zealand, (60-65), Gallizo J, Gargallo P, Saladrigues R and Salvador M, Lu D, Ye M, Hill M, Poeter E and Curtis G, Zhang C and R C Towards high-throughput gibbs sampling at scale Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, (397-408), Nori A and Sharma R Termination proofs from tests Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering, (246-256), Ren S and He Y COCA Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, (1-12), Chen Z, Mukherjee A, Liu B, Hsu M, Castellanos M and Ghosh R Discovering coherent topics using general knowledge Proceedings of the 22nd ACM international conference on Information & Knowledge Management, (209-218), Jakovits P and Srirama S Clustering on the cloud Proceedings of the Second Nordic Symposium on Cloud Computing & Internet Technologies, (64-71), Arellano C and Dahyot R Robust Bayesian fitting of 3D morphable model Proceedings of the 10th European Conference on Visual Media Production, (1-10), Sun J, Garibaldi J, Krasnogor N and Zhang Q, Zhang X A bayesian approach for modeling and analysis of call center arrivals Proceedings of the 2013 Winter Simulation Conference: Simulation: Making Decisions in a Complex World, (713-723), Taylor S, Brailsford S, Chick S, L'Ecuyer P, Macal C and Nelson B Modeling and simulation grand challenges Proceedings of the 2013 Winter Simulation Conference: Simulation: Making Decisions in a Complex World, (1269-1282), Babagholami-Mohamadabadi B, Jourabloo A, Zolfaghari M and Manzuri-Shalmani M Bayesian supervised dictionary learning Proceedings of the 2013 UAI Conference on Application Workshops: Big Data meet Complex Models and Models for Spatial, Temporal and Network Data - Volume 1024, (11-19), Yuan Z, Sang J, Liu Y and Xu C Latent feature learning in social media network Proceedings of the 21st ACM international conference on Multimedia, (253-262), Briand L, Falessi D, Nejati S, Sabetzadeh M and Yue T Research-based innovation Proceedings of the 15th international conference on Model Driven Engineering Languages and Systems, (793-809), Wang T, Srivatsa M and Liu L Fine-grained access control of personal data Proceedings of the 17th ACM symposium on Access Control Models and Technologies, (145-156), Sang J and Xu C Right buddy makes the difference Proceedings of the 20th ACM international conference on Multimedia, (19-28), Wright J and Leyton-Brown K Behavioral game theoretic models Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2, (921-930), Du L, Buntine W and Jin H Modelling sequential text with an adaptive topic model Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, (535-545), Schirone D and Torkan G Marketing strategies Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part II, (17-26), Voglreiter P, Steinberger M, Schmalstieg D and Kainz B Volumetric real-time particle-based representation of large unstructured tetrahedral polygon meshes Proceedings of the 2012 international conference on Mesh Processing in Medical Image Analysis, (159-168), Li J and Tao D Sampling normal distribution restricted on multiple regions Proceedings of the 19th international conference on Neural Information Processing - Volume Part I, (492-500), Zhai K, Boyd-Graber J, Asadi N and Alkhouja M Mr. LDA Proceedings of the 21st international conference on World Wide Web, (879-888), Deutch D, Greenshpan O, Kostenko B and Milo T Declarative platform for data sourcing games Proceedings of the 21st international conference on World Wide Web, (779-788), Ho Q, Eisenstein J and Xing E Document hierarchies from text and links Proceedings of the 21st international conference on World Wide Web, (739-748), Wood F, Gasthaus J, Archambeau C, James L and Teh Y, Nobari S, Lu X, Karras P and Bressan S Fast random graph generation Proceedings of the 14th International Conference on Extending Database Technology, (331-342), Fattal R Blue-noise point sampling using kernel density model ACM SIGGRAPH 2011 papers, (1-12), Jampani R, Xu F, Wu M, Perez L, Jermaine C and Haas P, Low Y, Agarwal D and Smola A Multiple domain user personalization Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, (123-131), Ramos H, Guidoni D, Boukerche A, Nakamura E, Frery A and Loureiro A Topology-related modeling and characterization of wireless sensor networks Proceedings of the 8th ACM Symposium on Performance evaluation of wireless ad hoc, sensor, and ubiquitous networks, (33-40), Boshmaf Y, Muslukhov I, Beznosov K and Ripeanu M The socialbot network Proceedings of the 27th Annual Computer Security Applications Conference, (93-102), Srirama S, Batrashev O, Jakovits P and Vainikko E, Robert C Simulation in statistics Proceedings of the Winter Simulation Conference, (108-119), Botev Z, L'Ecuyer P and Tuffin B An importance sampling method based on a one-step look-ahead density from a Markov chain Proceedings of the Winter Simulation Conference, (528-539), Milo T Crowd-based data sourcing Proceedings of the 7th international conference on Databases in Networked Information Systems, (64-67), Guyader A, Hengartner N and Matzner-Lber E, Elguebaly T and Bouguila N Infinite generalized gaussian mixture modeling and applications Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I, (201-210), Bhatnagar N, Bogdanov A and Mossel E The computational complexity of estimating MCMC convergence time Proceedings of the 14th international workshop and 15th international conference on Approximation, randomization, and combinatorial optimization: algorithms and techniques, (424-435), Wilson A and Ghahramani Z Generalised Wishart processes Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence, (736-744), Somaiya M, Jermaine C and Ranka S Mixture models for learning low-dimensional roles in high-dimensional data Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, (909-918), Fonseca R, Dilillo L, Bosio A, Girard P, Pravossoudovitch S, Virazel A and Badereddine N A statistical simulation method for reliability analysis of SRAM core-cells Proceedings of the 47th Design Automation Conference, (853-856), Ribeiro B and Towsley D Estimating and sampling graphs with multidimensional random walks Proceedings of the 10th ACM SIGCOMM conference on Internet measurement, (390-403), Altuzarra A, Moreno-Jimnez J and Salvador M, Cohen S, Blei D and Smith N Variational inference for adaptor grammars Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, (564-572), Wesolkowski S, Sakr Z, Di Stefano B and Lawniczak A Multiobjective evolutionary optimization of a transportation fleet with a modified monetary cost function Proceedings of the 2010 Summer Computer Simulation Conference, (147-153), Zhou E and Chen X A new populatoin-based simulated annealing algorithm Proceedings of the Winter Simulation Conference, (1211-1222), Bardenet R and Kgl B Surrogating the surrogate Proceedings of the 27th International Conference on International Conference on Machine Learning, (55-62), Blei D and Frazier P Distance dependent Chinese restaurant processes Proceedings of the 27th International Conference on International Conference on Machine Learning, (87-94), Chari L, Pesquet J, Tourneret J, Ciuciu P and Benazza-Benyahia A, Chellappa R, Sankaranarayanan A, Veeraraghavan A and Turaga P, Blackmore L, Ono M, Bektassov A and Williams B, Eches O, Dobigeon N, Mailhes C and Tourneret J, Elguebaly T and Bouguila N Bayesian learning of generalized gaussian mixture models on biomedical images Proceedings of the 4th IAPR TC3 conference on Artificial Neural Networks in Pattern Recognition, (207-218), Barker D, Hill S and Mukherjee S MC4 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics, (431-442), Brunel N and D'Alch-Buc F Flow-based Bayesian estimation of nonlinear differential equations for modeling biological networks Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics, (443-454), Cussens J Approximate bayesian computation for the parameters of PRISM programs Proceedings of the 20th international conference on Inductive logic programming, (38-46), Frhwirth-Schnatter S, Frhwirth R, Held L and Rue H, Yang L, Nehab D, Sander P, Sitthi-amorn P, Lawrence J and Hoppe H Amortized supersampling ACM SIGGRAPH Asia 2009 papers, (1-12), Johnson M and Goldwater S Improving nonparameteric Bayesian inference Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, (317-325), Chari L, Benazza-Benyahia A, Pesquet J and Ciuciu P Wavelet-based parallel MRI regularization using bivariate sparsity promoting priors Proceedings of the 16th IEEE international conference on Image processing, (1705-1708), Chen B, Kifer D, LeFevre K and Machanavajjhala A, Miyazato K, Kimura A, Takagi S and Yamato J Real-time estimation of human visual attention with dynamic bayesian network and MCMC-based particle filter Proceedings of the 2009 IEEE international conference on Multimedia and Expo, (250-257), Iso K Web-based topic language modeling for audio indexing Proceedings of the 2009 IEEE international conference on Multimedia and Expo, (826-829), Graham R and Corts J Distributed sampling of random fields with unknown covariance Proceedings of the 2009 conference on American Control Conference, (4543-4548), Peng R, Chen R and Farhang-Boroujeny B Low complexity Markov chain Monte Carlo detector for channels with intersymbol interference Proceedings of the 2009 IEEE international conference on Communications, (1908-1912), Akoum S, Peng R, Chen R and Farhang-Boroujeny B Markov chain Monte Carlo detection methods for high SNR regimes Proceedings of the 2009 IEEE international conference on Communications, (3026-3030), Dobigeon N, Moussaoui S, Coulon M, Tourneret J and Hero A, Panayirci E, Dogan H and Poor H A Gibbs sampling based MAP detection algorithm for OFDM over rapidly varying mobile radio channels Proceedings of the 28th IEEE conference on Global telecommunications, (6574-6579), Dobigeon N, Moussaoui S, Tourneret J and Carteret C, Yang L, Nehab D, Sander P, Sitthi-amorn P, Lawrence J and Hoppe H, Hansen M, Hassibi B, Dimakis A and Xu W Near-optimal detection in MIMO systems using Gibbs sampling Proceedings of the 28th IEEE conference on Global telecommunications, (2761-2766), Salmon J and Le Pennec E NL-Means and aggregation procedures Proceedings of the 16th IEEE international conference on Image processing, (2941-2944), Kifer D Attacks on privacy and deFinetti's theorem Proceedings of the 2009 ACM SIGMOD International Conference on Management of data, (127-138), Sehgal M, Gondal I, Dooley L and Coppel R, Zhou D, Bian J, Zheng S, Zha H and Giles C Exploring social annotations for information retrieval Proceedings of the 17th international conference on World Wide Web, (715-724), Scherrer A, Borgnat P, Fleury E, Guillaume J and Robardet C A methodology to identify characteristics of the dynamic of mobile networks Proceedings of the 4th Asian Conference on Internet Engineering, (59-62), Kyriazopoulou-Panagiotopoulou S, Kontoyiannis I and Meyn S Control variates as screening functions Proceedings of the 3rd International Conference on Performance Evaluation Methodologies and Tools, (1-9), Wesolkowski S and Billyard A The Stochastic Fleet Estimation (SaFE) model Proceedings of the 2008 Spring simulation multiconference, (1-5), Gao J and Johnson M A comparison of Bayesian estimators for unsupervised Hidden Markov Model POS taggers Proceedings of the Conference on Empirical Methods in Natural Language Processing, (344-352), Johnson M Unsupervised word segmentation for Sesotho using Adaptor Grammars Proceedings of the Tenth Meeting of ACL Special Interest Group on Computational Morphology and Phonology, (20-27), Moussaoui S, Hauksdttir H, Schmidt F, Jutten C, Chanussot J, Brie D, Dout S and Benediktsson J, Keller G, Chaffey-Millar H, Chakravarty M, Stewart D and Barner-Kowollik C Specialising simulator generators for high-performance Monte-Carlo methods Proceedings of the 10th international conference on Practical aspects of declarative languages, (116-132), Nagata K and Watanabe S Design of exchange Monte Carlo method for Bayesian learning in normal mixture models Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I, (696-706), Pellacini F and Lawrence J AppWand ACM SIGGRAPH 2007 papers, (54-es), Ukkonen T, Sarjakoski T and Oksanen J Distributed computation of drainage basin delineations from uncertain digital elevation models Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems, (1-8), Nehab D, Sander P, Lawrence J, Tatarchuk N and Isidoro J Accelerating real-time shading with reverse reprojection caching Proceedings of the 22nd ACM SIGGRAPH/EUROGRAPHICS symposium on Graphics hardware, (25-35), Wu M and Jermaine C A Bayesian method for guessing the extreme values in a data set?
