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Sequential Monte Carlo without likelihoods
- S. Sisson, Y. Fan, Mark M. Tanaka
- Computer ScienceProceedings of the National Academy of Sciences
- 6 February 2007
This work proposes a sequential Monte Carlo sampler that convincingly overcomes inefficiencies of existing methods and demonstrates its implementation through an epidemiological study of the transmission rate of tuberculosis.
Statistical Inference and Simulation for Spatial Point Processes
- S. Sisson
A comparative review of dimension reduction methods in approximate Bayesian computation
This article provides a comprehensive review and comparison of the performance of the principal methods of dimension reduction proposed in the ABC literature, split into three nonmutually exclusive classes consisting of best subset selection methods, projection techniques and regularization.
Handbook of Approximate Bayesian Computation
Using Approximate Bayesian Computation to Estimate Tuberculosis Transmission Parameters From Genotype Data
An approximate Bayesian computational method in combination with a stochastic model of tuberculosis transmission and mutation of a molecular marker is used to estimate the net transmission rate, the doubling time, and the reproductive value of the pathogen.
A fully probabilistic approach to extreme rainfall modeling
Development of a formal likelihood function for improved Bayesian inference of ephemeral catchments
The application of formal Bayesian inferential approaches in hydrologic modeling is often criticized for requiring explicit assumptions to be made about the distribution of the errors via the…
Likelihood-free Markov chain Monte Carlo
To appear to MCMC handbook, S. P. Brooks, A. Gelman, G. Jones and X.-L. Meng (eds), Chapman & Hall.
In defence of model‐based inference in phylogeography
It is argued that ABC is a statistically valid approach, alongside other computational statistical techniques that have been successfully used to infer parameters and compare models in population genetics, and is encouraging researchers to study and use model‐based inference in population Genetics.