Computational methods for complex stochastic systems: a review of some alternatives to MCMC
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[1] A. Doucet,et al. Particle filtering for partially observed Gaussian state space models , 2002 .
[2] A. Doucet,et al. Particle Motions in Absorbing Medium with Hard and Soft Obstacles , 2004 .
[3] O. Pybus,et al. Bayesian coalescent inference of past population dynamics from molecular sequences. , 2005, Molecular biology and evolution.
[4] Paul Fearnhead,et al. Particle filters for mixture models with an unknown number of components , 2004, Stat. Comput..
[5] W S Watkins,et al. Origins and affinities of modern humans: a comparison of mitochondrial and nuclear genetic data. , 1995, American journal of human genetics.
[6] Ajay Jasra,et al. On population-based simulation for static inference , 2007, Stat. Comput..
[7] Nicolas Chopin,et al. Inference and model choice for sequentially ordered hidden Markov models , 2007 .
[8] X. Jin. Factor graphs and the Sum-Product Algorithm , 2002 .
[9] M. De Iorio,et al. Importance sampling on coalescent histories. I , 2004, Advances in Applied Probability.
[10] P. Fearnhead. MCMC, sufficient statistics and particle filters. , 2002 .
[11] Daniel Falush,et al. A bimodal pattern of relatedness between the Salmonella Paratyphi A and Typhi genomes: convergence or divergence by homologous recombination? , 2006, Genome research.
[12] N. Metropolis,et al. Equation of State Calculations by Fast Computing Machines , 1953, Resonance.
[13] Brian D. Ripley,et al. Stochastic Simulation , 2005 .
[14] P. Fearnhead,et al. On‐line inference for hidden Markov models via particle filters , 2003 .
[15] J. Wakeley. Coalescent Theory: An Introduction , 2008 .
[16] A. Doucet,et al. Monte Carlo Smoothing for Nonlinear Time Series , 2004, Journal of the American Statistical Association.
[17] M. Achtman,et al. Multilocus sequence typing: a portable approach to the identification of clones within populations of pathogenic microorganisms. , 1998, Proceedings of the National Academy of Sciences of the United States of America.
[18] Yi-Ching Yao. Estimation of a Noisy Discrete-Time Step Function: Bayes and Empirical Bayes Approaches , 1984 .
[19] P. Moral,et al. Sequential Monte Carlo for Bayesian Computation , 2006 .
[20] Yuguo Chen,et al. Stopping‐time resampling for sequential Monte Carlo methods , 2005 .
[21] Jun S. Liu,et al. Bayesian inference on biopolymer models , 1999, Bioinform..
[22] C. J-F,et al. THE COALESCENT , 1980 .
[23] P. Moral,et al. On the stability of interacting processes with applications to filtering and genetic algorithms , 2001 .
[24] W. Gilks,et al. Following a moving target—Monte Carlo inference for dynamic Bayesian models , 2001 .
[25] B. Delyon,et al. Simulation of conditioned diffusion and application to parameter estimation , 2006 .
[26] Darren J. Wilkinson,et al. Bayesian inference for a discretely observed stochastic kinetic model , 2008, Stat. Comput..
[27] P. Fearnhead,et al. Exact filtering for partially observed continuous time models , 2004 .
[28] S. Tavaré,et al. Unrooted genealogical tree probabilities in the infinitely-many-sites model. , 1995, Mathematical biosciences.
[29] Kent E. Holsinger,et al. Model fitting and inference under latent equilibrium processes , 2007, Stat. Comput..
[30] A. Gallant,et al. Numerical Techniques for Maximum Likelihood Estimation of Continuous-Time Diffusion Processes , 2002 .
[31] P. Fearnhead. The common ancestor at a nonneutral locus , 2002, Journal of Applied Probability.
[32] T. Ferguson. A Bayesian Analysis of Some Nonparametric Problems , 1973 .
[33] M. West,et al. Bayesian forecasting and dynamic models , 1989 .
[34] L. Rogers,et al. Diffusions, Markov Processes and Martingales: References , 2000 .
[35] Radford M. Neal. Markov Chain Sampling for Non-linear State Space Models Using Embedded Hidden Markov Models , 2003, math/0305039.
[36] P. Green. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination , 1995 .
[37] Nando de Freitas,et al. Sequential Monte Carlo in Practice , 2001 .
[38] Daniel J. Wilson,et al. Rapid Evolution and the Importance of Recombination to the Gastroenteric Pathogen Campylobacter jejuni , 2008, Molecular biology and evolution.
[39] M. Pitt. Smooth Particle Filters for Likelihood Evaluation and Maximisation , 2002 .
[40] J. Crow,et al. THE NUMBER OF ALLELES THAT CAN BE MAINTAINED IN A FINITE POPULATION. , 1964, Genetics.
[41] R. Griffiths,et al. Inference from gene trees in a subdivided population. , 2000, Theoretical population biology.
[42] Timothy J. Robinson,et al. Sequential Monte Carlo Methods in Practice , 2003 .
[43] S. Kou,et al. Equi-energy sampler with applications in statistical inference and statistical mechanics , 2005, math/0507080.
[44] Dani Gamerman,et al. Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Second Edition , 2006 .
[45] P. Moral. Feynman-Kac Formulae: Genealogical and Interacting Particle Systems with Applications , 2004 .
[46] M. Suchard,et al. Joint Bayesian estimation of alignment and phylogeny. , 2005, Systematic biology.
[47] O. Stramer,et al. On Simulated Likelihood of Discretely Observed Diffusion Processes and Comparison to Closed-Form Approximation , 2007 .
[48] M. Feldman,et al. Genetic Structure of Human Populations , 2002, Science.
[49] D. Wilkinson,et al. Bayesian Inference for Stochastic Kinetic Models Using a Diffusion Approximation , 2005, Biometrics.
[50] M. Stephens. Times on trees, and the age of an allele. , 2000, Theoretical population biology.
[51] J. Felsenstein,et al. A Hidden Markov Model approach to variation among sites in rate of evolution. , 1996, Molecular biology and evolution.
[52] David Madigan,et al. A Sequential Monte Carlo Method for Bayesian Analysis of Massive Datasets , 2003, Data Mining and Knowledge Discovery.
[53] P. Donnelly,et al. Approximate likelihood methods for estimating local recombination rates , 2002 .
[54] N. Gordon,et al. Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .
[55] S. L. Scott. Bayesian Analysis of a Two-State Markov Modulated Poisson Process , 1999 .
[56] Biing-Hwang Juang,et al. Hidden Markov Models for Speech Recognition , 1991 .
[57] Paul Fearnhead,et al. Exact Bayesian curve fitting and signal segmentation , 2005, IEEE Transactions on Signal Processing.
[58] H. Künsch. Recursive Monte Carlo filters: algorithms and theoretical analysis , 2005 .
[59] Jun S. Liu,et al. Metropolized independent sampling with comparisons to rejection sampling and importance sampling , 1996, Stat. Comput..
[60] Peter Donnelly,et al. A countable representation of the Fleming-Viot measure-valued diffusion , 1996 .
[61] G. Kitagawa. Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models , 1996 .
[62] Peter Donnelly,et al. Assessing population differentiation and isolation from single‐nucleotide polymorphism data , 2002 .
[63] M. De Iorio,et al. Importance sampling on coalescent histories. II: Subdivided population models , 2004, Advances in Applied Probability.
[64] G. Roberts,et al. On inference for partially observed nonlinear diffusion models using the Metropolis–Hastings algorithm , 2001 .
[65] Jun S. Liu,et al. Mixture Kalman filters , 2000 .
[66] S. L. Scott. Bayesian Methods for Hidden Markov Models , 2002 .
[67] Jun S. Liu,et al. Rejection Control and Sequential Importance Sampling , 1998 .
[68] L. Rogers,et al. Diffusions, Markov processes, and martingales , 1979 .
[69] L. Rabiner,et al. An introduction to hidden Markov models , 1986, IEEE ASSP Magazine.
[70] Darren J. Wilkinson,et al. Bayesian Sequential Inference for Stochastic Kinetic Biochemical Network Models , 2006, J. Comput. Biol..
[71] E. Lander,et al. Construction of multilocus genetic linkage maps in humans. , 1987, Proceedings of the National Academy of Sciences of the United States of America.
[72] O. Cappé,et al. Population Monte Carlo , 2004 .
[73] Michael A. West,et al. Combined Parameter and State Estimation in Simulation-Based Filtering , 2001, Sequential Monte Carlo Methods in Practice.
[74] Problems with computational methods in population ge - , 1999 .
[75] Michael I. Jordan,et al. Probabilistic Networks and Expert Systems , 1999 .
[76] Michael A. West,et al. Bayesian forecasting and dynamic models (2nd ed.) , 1997 .
[77] P. Fearnhead,et al. Particle filters for partially observed diffusions , 2007, 0710.4245.
[78] N. Chopin. A sequential particle filter method for static models , 2002 .
[79] R. E. Kalman,et al. New Results in Linear Filtering and Prediction Theory , 1961 .
[80] Jun S. Liu,et al. Blind Deconvolution via Sequential Imputations , 1995 .
[81] H. Kunsch. Recursive Monte Carlo filters: Algorithms and theoretical analysis , 2006, math/0602211.
[82] D. Blackwell,et al. Ferguson Distributions Via Polya Urn Schemes , 1973 .
[83] P Donnelly,et al. Coalescents and genealogical structure under neutrality. , 1995, Annual review of genetics.
[84] J. Hartigan,et al. A Bayesian Analysis for Change Point Problems , 1993 .
[85] T. Alderweireld,et al. A Theory for the Term Structure of Interest Rates , 2004, cond-mat/0405293.
[86] Neil J. Gordon,et al. Editors: Sequential Monte Carlo Methods in Practice , 2001 .
[87] P. Fearnhead. Using Random Quasi-Monte-Carlo Within Particle Filters, With Application to Financial Time Series , 2005 .
[88] P. Fearnhead,et al. Efficient Online Inference for Multiple Changepoint Problems , 2006, 2006 IEEE Nonlinear Statistical Signal Processing Workshop.
[89] Simon J. Godsill,et al. On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..
[90] Darren J. Wilkinson. Stochastic Modelling for Systems Biology , 2006 .
[91] Xiao-Li Meng,et al. SIMULATING RATIOS OF NORMALIZING CONSTANTS VIA A SIMPLE IDENTITY: A THEORETICAL EXPLORATION , 1996 .
[92] G. Roberts,et al. Monte Carlo Maximum Likelihood Estimation for Discretely Observed Diffusion Processes , 2009, 0903.0290.
[93] Jun S. Liu,et al. Sequential Imputations and Bayesian Missing Data Problems , 1994 .
[94] P. Fearnhead,et al. Exact and computationally efficient likelihood‐based estimation for discretely observed diffusion processes (with discussion) , 2006 .
[95] Sean R. Eddy,et al. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids , 1998 .
[96] O. Stramer,et al. Asymptotics of an Efficient Monte Carlo Estimation for the Transition Density of Diffusion Processes , 2007 .
[97] Daniel Falush,et al. Mismatch induced speciation in Salmonella: model and data , 2006, Philosophical Transactions of the Royal Society B: Biological Sciences.
[98] P. Donnelly,et al. Association mapping in structured populations. , 2000, American journal of human genetics.
[99] A. Pedersen. A new approach to maximum likelihood estimation for stochastic differential equations based on discrete observations , 1995 .
[100] J. Hartigan,et al. Product Partition Models for Change Point Problems , 1992 .
[101] S. E. Ahmed,et al. Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference , 2008, Technometrics.
[102] G. Peters,et al. Monte Carlo Approximations for General State-Space Models , 1998 .
[103] P. Fearnhead,et al. Improved particle filter for nonlinear problems , 1999 .
[104] M. Pitt,et al. Filtering via Simulation: Auxiliary Particle Filters , 1999 .
[105] Gareth O. Roberts,et al. Non-centred parameterisations for hierarchical models and data augmentation. , 2003 .
[106] P. Donnelly,et al. Inference in molecular population genetics , 2000 .
[107] P. Kloeden,et al. Numerical Solution of Stochastic Differential Equations , 1992 .
[108] Jean-Michel Marin,et al. Iterated importance sampling in missing data problems , 2006, Comput. Stat. Data Anal..
[109] P. Fearnhead,et al. An improved particle filter for non-linear problems , 1999 .
[110] P. Fearnhead. Markov chain Monte Carlo, Sufficient Statistics, and Particle Filters , 2002 .
[111] Pierre L'Ecuyer,et al. A Randomized Quasi-Monte Carlo Simulation Method for Markov Chains , 2006, Oper. Res..
[112] M. Stephens,et al. Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. , 2003, Genetics.
[113] P. Fearnhead,et al. An exact Gibbs sampler for the Markov‐modulated Poisson process , 2006 .
[114] Hugh Griffiths,et al. IEE Proceedings - Radar, Sonar and Navigation , 2004 .
[115] The Common Ancestor At A Non-Neutral Locus , 2001 .
[116] Paul Fearnhead,et al. Bayesian Analysis of Isochores , 2009 .
[117] Paul Fearnhead,et al. Exact and efficient Bayesian inference for multiple changepoint problems , 2006, Stat. Comput..
[118] Geir Storvik,et al. Particle filters for state-space models with the presence of unknown static parameters , 2002, IEEE Trans. Signal Process..
[119] P. Donnelly,et al. Estimating recombination rates from population genetic data. , 2001, Genetics.
[120] P. Donnelly,et al. Inference of population structure using multilocus genotype data. , 2000, Genetics.
[121] L. Baum,et al. A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains , 1970 .
[122] Jun S. Liu,et al. Sequential Monte Carlo methods for dynamic systems , 1997 .
[123] Alexei J Drummond,et al. Estimating mutation parameters, population history and genealogy simultaneously from temporally spaced sequence data. , 2002, Genetics.
[124] P. Moral,et al. Sequential Monte Carlo samplers , 2002, cond-mat/0212648.