Kernel Methods in Approximate Bayesian Computation

[1]  Christian P. Robert,et al.  Bayesian computation: a summary of the current state, and samples backwards and forwards , 2015, Statistics and Computing.

[2]  Le Song,et al.  A unified kernel framework for nonparametric inference in graphical models ] Kernel Embeddings of Conditional Distributions , 2013 .

[3]  Mark M. Tanaka,et al.  Sequential Monte Carlo without likelihoods , 2007, Proceedings of the National Academy of Sciences.

[4]  M. Feldman,et al.  Population growth of human Y chromosomes: a study of Y chromosome microsatellites. , 1999, Molecular biology and evolution.

[5]  Bernhard Schölkopf,et al.  The connection between regularization operators and support vector kernels , 1998, Neural Networks.

[6]  Franck Jabot,et al.  EasyABC: performing efficient approximate Bayesian computation sampling schemes using R , 2013 .

[7]  Bernhard Schölkopf,et al.  Detecting low-complexity unobserved causes , 2011, UAI.

[8]  Paul Marjoram,et al.  Markov chain Monte Carlo without likelihoods , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[9]  Paul Marjoram,et al.  Statistical Applications in Genetics and Molecular Biology Approximately Sufficient Statistics and Bayesian Computation , 2011 .

[10]  Bernhard Schölkopf,et al.  Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders , 2013, UAI.

[11]  S. Wood.  Statistical inference for noisy nonlinear ecological dynamic systems , 2010, Nature.

[12]  Richard R. Hudson,et al.  ms ­ a program for generating samples under neutral models , 2004 .

[13]  Richard R. Hudson,et al.  Generating samples under a Wright-Fisher neutral model of genetic variation , 2002, Bioinform..

[14]  L. Excoffier,et al.  Efficient Approximate Bayesian Computation Coupled With Markov Chain Monte Carlo Without Likelihood , 2009, Genetics.

[15]  Arnaud Doucet,et al.  An adaptive sequential Monte Carlo method for approximate Bayesian computation , 2011, Statistics and Computing.

[16]  N. Metropolis,et al.  The Monte Carlo method. , 1949 .

[17]  Andrew R. Francis,et al.  Using Approximate Bayesian Computation to Estimate Tuberculosis Transmission Parameters From Genotype Data , 2006, Genetics.

[18]  Simon Tavaré,et al.  Approximate Bayesian Computation and MCMC , 2004 .

[19]  S. A. Sisson,et al.  Overview of Approximate Bayesian Computation , 2018, 1802.09720.

[20]  S. Qin,et al.  Selection of the Number of Principal Components: The Variance of the Reconstruction Error Criterion with a Comparison to Other Methods† , 1999 .

[21]  W. Moore.  INFERRING PHYLOGENIES FROM mtDNA VARIATION: MITOCHONDRIAL‐GENE TREES VERSUS NUCLEAR‐GENE TREES , 1995, Evolution; international journal of organic evolution.

[22]  Ker-Chau Li,et al.  Sliced Inverse Regression for Dimension Reduction , 1991 .

[23]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[24]  M. Gutmann,et al.  Fundamentals and Recent Developments in Approximate Bayesian Computation , 2016, Systematic biology.

[25]  Paul Fearnhead,et al.  On the Asymptotic Efficiency of Approximate Bayesian Computation Estimators , 2015, 1506.03481.

[26]  Christian P. Robert,et al.  Monte Carlo Statistical Methods (Springer Texts in Statistics) , 2005 .

[27]  David Welch,et al.  Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems , 2009, Journal of The Royal Society Interface.

[28]  C. Robert,et al.  ABC likelihood-free methods for model choice in Gibbs random fields , 2008, 0807.2767.

[29]  Alex Smola,et al.  Kernel methods in machine learning , 2007, math/0701907.

[30]  K. Fukumizu,et al.  Kernel approximate Bayesian computation in population genetic inferences , 2012, Statistical applications in genetics and molecular biology.

[31]  D. Balding,et al.  Statistical Applications in Genetics and Molecular Biology On Optimal Selection of Summary Statistics for Approximate Bayesian Computation , 2011 .

[32]  Alexander J. Smola,et al.  Hilbert space embeddings of conditional distributions with applications to dynamical systems , 2009, ICML '09.