Approximate Inference for Generic Likelihoods via Density-Preserving GMM Simplification

Phylogeographic Inference Using Approximate Likelihoods 1 Running Title : Phylogeography with Approximate Likelihoods 2 3 4 5 6 7 8 9 10 11 Brian

Playing Russian Roulette with Intractable Likelihoods

A general scheme to exploit Exact-Approximate MCMC methodology for intractable likelihoods is suggested. By representing the intractable likelihood as an infinite Maclaurin or Geometric series expansion, unbiased estimates of the likelihood can be obtained by finite time stochastic truncations of the series via Russian Roulette sampling. Whilst the estimates of the intractable likelihood are un...

Fast $\epsilon$-free Inference of Simulation Models with Bayesian Conditional Density Estimation

Many statistical models can be simulated forwards but have intractable likelihoods. Approximate Bayesian Computation (ABC) methods are used to infer properties of these models from data. Traditionally these methods approximate the posterior over parameters by conditioning on data being inside an -ball around the observed data, which is only correct in the limit →0. Monte Carlo methods can then ...

On the equivalence of prospective and retrospective likelihood methods in case-control studies

We present new approaches to analyzing case-control studies using prospective likelihood methods. In the classical framework, we extend the equality of the profile likelihoods to the Barndorff-Nielsen modified profile likelihoods for prospective and retrospective models. This enables simple and accurate approximate conditional inference for stratified case-control studies of moderate stratum si...

Hierarchical Gaussian Mixture Model

Gaussian mixture models (GMMs) are a convenient and essential tool for the estimation of probability density functions. Although GMMs are used in many research domains from image processing to machine learning, this statistical mixture modeling is usually complex and further needs to be simplified. In this paper, we present a GMM simplification method based on a hierarchical clustering algorith...