Publications

Publications in 2015

W. Li, P. Fearnhead.
Behaviour of ABC for Big Data.

U. Picchini, R. Anderson.
Approximate maximum likelihood estimation using data-cloning ABC.

E.E.O. Ishida, S.D.P. Vitenti, M. Penna-Lima, J. Cisewski, R.S. de Souza, A.M.M. Trindade, E. Cameron, V.C. Busti, (COIN collaboration).
cosmoabc: Likelihood-free inference via Population Monte Carlo Approximate Bayesian Computation.

J. Li, D.J. Nott, Y. Fan, S.A. Sisson.
Extending approximate Bayesian computation methods to high dimensions via Gaussian copula.

C. Grazian, B. Liseo.
Approximate Bayesian Computation for Copula Estimation.

E. Meeds, R. Leenders, M. Welling.
Hamiltonian ABC.

J.-J. Forneron, S. Ng.
The ABC of Simulation Estimation with Auxiliary Statistics.

Publications in 2014

F. Jabot, G. Lagarrigues, B. Courbaud, N. Dumoulin.
A comparison of emulation methods for Approximate Bayesian Computation.

E. Cameron, A.N. Pettitt.
Approximate Bayesian Computation for Astronomical Model Analysis: A Case Study in Galaxy Demographics and Morphological Transformation at High Redshift.

P. Pudlo, J.-M. Marin, A. Estoup, J.-M. Cornuet, M. Gautier, C.P. Robert.
ABC model choice via random forests.

Y. Murakami.
Bayesian Parameter Inference and Model Selection by Population Annealing in Systems Biology.

D. Prangle.
Lazy ABC.

E. Meeds, M. Welling.
GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation.

R. Wilkinson.
Accelerating ABC methods using Gaussian processes.

M. Chiachio, J.L. Beck, J. Chiachio, G. Rus.
Approximate Bayesian Computation by Subset Simulation.

C. Grazian, B. Liseo.
Approximate Integrated Likelihood via ABC methods.

R.C. Crackel, J.M. Flegal.
Approximate Bayesian computation for a flexible class of bivariate beta distributions.

J. Stoehr, P. Pudlo, L. Cucala.
Geometric summary statistics for ABC model choice between hidden Gibbs random fields.

Publications in 2013

E. Ruli, N. Sartori, L. Ventura.
Approximate Bayesian Computation with composite score functions.

S. Barber, J. Voss, M. Webster.
The Rate of Convergence for Approximate Bayesian Computation.

A. Jasra, N. Kantas, E. Ehrlich.
Approximate Inference for Observation Driven Time Series Models with Intractable Likelihoods.

M. Girolami, A.-M. Lyne, H. Strathmann, D. Simpson, Y. Atchade.
Playing Russian Roulette with Intractable Likelihoods.

M. Hainy, W.G. Müller, H. Wagner.
Likelihood-free Simulation-based Optimal Design.

C. Dimitrakakis, N. Tziortziotis.
ABC Reinforcement Learning.

V.K. Mansinghka, T.D. Kulkarni, Y.N. Perov, J.B. Tenenbaum.
Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs.

U. Picchini, J.L. Forman.
Accelerating inference for diffusions observed with measurement error and large sample sizes using Approximate Bayesian Computation: A case study.

O. Chkrebtii, E.K. Cameron, D.A. Campbell, E.M. Bayne.
Transdimensional Approximate Bayesian Computation for Inference on Invasive Species Models with Latent Variables of Unknown Dimension.

E. Ruli, N. Sartori, L. Ventura.
Approximate Bayesian Computation with composite score functions.

U. Picchini.
Inference for SDE models via Approximate Bayesian Computation.

O. Ratmann, A. Camacho, A. Meijer, G. Donker.
Statistical modelling of summary values leads to accurate Approximate Bayesian Computations.

D. Prangle, P. Fearnhead , M.P. Cox, P. J. Biggs, N. P. French.
Semi-automatic selection of summary statistics for ABC model choice.

D. Prangle, M.G.B. Blum, G. Popovic, S. A. Sisson.
Diagnostic tools of approximate Bayesian computation using the coverage property.

E. O. Buzbas and N. A. Rosenberg
AABC: Approximate approximate Bayesian computation when simulating a large number of data sets is computationally infeasible.

Publications in 2012

Y. Fan, D. J. Nott, S. A. Sisson                                                                                            Approximate Bayesian computation via regression density estimation.

A. Lee, K. Latuszynski.
Variance bounding and geometric ergodicity of Markov chain Monte Carlo kernels for approximate Bayesian computation.

Gérard Biau, Frédéric Cérou, Arnaud Guyader.
New Insights into Approximate Bayesian Computation.

E. O. Buzbas.
 “On ‘Estimating species trees using approximate Bayesian computation’ ”
Molecular Phylogenetics and Evolution, 65: 1014-1016.

E. Ehrlich, A. Jasra, N. Kantas.
Static Parameter Estimation for ABC Approximations of Hidden Markov Models.

D. Silk, S. Filippi, M.P.H. Stumpf.
Optimizing Threshold – Schedules for Approximate Bayesian Computation Sequential Monte Carlo Samplers: Applications to Molecular Systems.

M. Sedki, J.-M. Cornuet, J.-M. Marin, P. Pudlo, C.P. Robert.
Efficient learning in ABC algorithms.

J.S. Martin, A. Jasra, S.S. Singh, N. Whiteley, E. McCoy.
Approximate Bayesian Computation for Smoothing.

R.G. Everitt.
Bayesian Parameter Estimation for Latent Markov Random Fields and Social Networks.

C. Barnes, S. Filippi, M.P.H. Stumpf, T. Thorne.
Considerate Approaches to Achieving Sufficiency for ABC model selection.

M.G.B. Blum, M.A. Nunes, D. Prangle, S. A. Sisson.
A comparative review of dimension reduction methods in approximate Bayesian computation.

P. Fearnhead, D. Prangle. (This is the final version of the Fearnhead and Prangle 2010 under literature tab)
Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation.
Journal of the Royal Statistical Society B 74 (3), pp. 1–28.
Discussions on the article.

K. L. Mengersen, P. Pudlo, C. P. Robert.
Bayesian computation via empirical likelihood.

D. J. Nott, Y. Fan, L. Marshall, S. A. Sisson.
Approximate Bayesian computation and Bayes linear analysis: towards high-dimensional ABC.

S. Filippi, C. Barnes, J. Cornebise, M.P.H. Stumpf.
On optimality of kernels for approximate Bayesian computation using sequential Monte Carlo.

Publications in 2011

M. Lenormand, F. Jabot, G. Deffuant.
Adaptive approximate Bayesian computation for complex models.

X. Didelot, R.G. Everitt, A.M. Johansen and D.J. Lawson.
Likelihood-free estimation of model evidence.

J.-M. Marin, N. Pillai, C.P. Robert, J. Rousseau.
Relevant statistics for Bayesian model choice.

J-M., Marin, Pudlo, P., Robert, C.P., Ryder, R.
Approximate Bayesian Computational Methods.

Robert, C.P., Cornuet, J-M., Marin, J-M., Pillai, N.S.
Lack of confidence in approximate Bayesian computation model choice.
Proceedings of the National Academy of Sciences USA 108: 15112-15117.

Fan, H.H. and Kubatko, L.S.
Estimating species trees using approximate Bayesian computation.
Molecular Phylogenetics and Evolution 59:354-363.

E. Lombaert,T. Guillemaud, C. E. Thomas, L. J. Lawson Handley, J. Li, S. Wang, H. Pang, I. Goryacheva, I. A. Zakharov, E. Jousselin, R. L. Poland, A. Migeon, J. Van Lenteren, P. De Clercq, N. Berkvens, W. Jones, A. Estoup.
Inferring the origin of populations introduced from a genetically structured native range by approximate Bayesian computation: case study of the invasive ladybird Harmonia axyridis
Molecular Ecology 20: 4654–467.

M. Baragatti, A. Grimaud, D. Pommeret.
Likelihood-Free Parallel Tempering.

P.D. Keightley, L. Eöry, D.L. Halligan, M. Kirkpatrick.
Inference of Mutation Parameters and Selective Constraint in Mammalian Coding Sequences by Approximate Bayesian Computation.
Genetics 187: 1153–116.

K. Csilléry, O. François, M.G.B. Blum.
abc: an R package for Approximate Bayesian Computation (ABC)

J.R. Row, R.J. Brooks, C.A. MacKinnon, A. Lawson, B.I. Crother, M. White, S.C. Lougheed.
Approximate Bayesian computation reveals the factors that influence genetic diversity and population structure of foxsnakes.
Journal of Evolutionary Biology 24: 2364–2377.

C.C. Drovandi, A.N. Pettitt.
Likelihood-free Bayesian estimation of multivariate quantile distributions.
Computational Statistics and Data Analysis 55: 2541–2556.

C.C. Drovandi, A.N. Pettitt.
Estimation of Parameters for Macroparasite Population Evolution Using Approximate Bayesian Computation.
Biometrics 67: 225–233.

S.J.Y. Laurent, A. Werzner, L. Excoffier, W. Stephan.
Approximate Bayesian Analysis of Drosophila melanogaster Polymorphism Data Reveals a Recent Colonization of Southeast Asia.
Molecular Biology and Evolution 28: 2041-2051.

J. Cussens
Approximate Bayesian Computation for the Parameters of PRISM Programs.
Lecture Notes in Computer Science 6489: 38-46.

O. Francois, G. Laval.
Deviance Information Criteria for Model Selection in Approximate Bayesian Computation.
Statistical Applications in Genetics and Molecular Biology 10: Article 33.

M. Lonergan, D. Thompson, L. Thomas, C. Duck.
An Approximate Bayesian Method Applied to Estimating the Trajectories of Four British Grey Seal (Halichoerus grypus ) Populations from Pup counts.
Journal of Marine Biology
Volume 2011, Article ID 597424.

W. Huang, N. Takebayashi, Y. Qi, M.J. Hickerson.
MTML-msBayes: Approximate Bayesian comparative phylogeographic inference from multiple taxa and multiple loci with rate heterogeneity
BMC Bioinformatics 12:1.

C.C. Drovandi, A.N. Pettitt.
Using Approximate Bayesian Computation to Estimate Transmission Rates of Nosocomial Pathogens.
Statistical Communications in Infectious Diseases 3: Article 2.

L. Excoffier and M. Foll.
fastsimcoal: a continuous-time coalescent simulator of genomic diversity under arbitrarily complex evolutionary scenarios.
Bioinformatics 27: 1332-1334.

C.C. Drovandi, A.N. Pettitt, M.J. Faddy.
Approximate Bayesian computation using indirect inference.
Journal of the Royal Statistical Society: Series C (Applied Statistics) 60: 317–337.

E. Haon-Lasportes, F. Carpentier, O. Martin, E.K. Klein, S. Soubeyrand.
Conditioning on Parameter Point Estimates in Approximate Bayesian Computation.
INRA Research Report No. 45 July 2011.

C.P. Robert.
Simulation in Statistics.

S. Barthelmé, N. Chopin.
ABC-EP: Expectation Propagation for Likelihood-free Bayesian Computation.

M.S. Ascunce, C.-C. Yang, J. Oakey, L. Calcaterra, W.-J. Wu, C.-J. Shih, J. Goudet, K.G. Ross, D. Shoemaker.
Global Invasion History of the Fire Ant Solenopsis invicta.
Science 331: 1066-1068.

I. Gronau, M.J. Hubisz, B. Gulko, C.G. Danko, A. Siepel.
Bayesian inference of ancient human demography from individual genome sequences.
Nature Genetics 43: 1031–1034.

T.A. Dean, S.S. Singh.
Asymptotic Behaviour of Approximate Bayesian Estimators.

M.G.B. Blum, M. Jakobsson.
Deep Divergences of Human Gene Trees and Models of Human Origins.
Molecular Biology and Evolution 28: 889-898.

O. Ratmann, P. Pudlo, S. Richardson, C.P. Robert.
Monte Carlo algorithms for model assessment via conflicting summaries.

A. Golightly, D.J. Wilkinson.
Bayesian parameter inference for stochastic biochemical network models using particle MCMC.