Likelihood-Free Inference by Ratio Estimation

Semiparametric Likelihood Ratio Inference Revisited

Semiparametric Likelihood Ratio Inference Revisited

2000 We extend the Semiparametric Likelihood Ratio Theorem of Murphy and Van del' Vaart for one-dimensional to Euclidean paramet(;rs of auy dimension. The as:VIrlptotic distribution of the likelihood ratio statistic for testing a k-dimensional Euclidean paramet'"r is shown to be the usual under the null hypothesis. This result is useful not only for testing purposes but also in forming likeliho...

Maximum Likelihood Estimation and Likelihood-ratio Tests

The method of maximum likelihood (ML), introduced by Fisher (1921), is widely used in human and quantitative genetics and we draw upon this approach throughout the book, especially in Chapters 13–16 (mixture distributions) and 26–27 (variance component estimation). Weir (1996) gives a useful introduction with genetic applications, while Kendall and Stuart (1979) and Edwards (1992) provide more ...

Likelihood Almost Free Inference Networks

Variational inference for latent variable models is prevalent in various machine learning problems, typically solved by maximizing the Evidence Lower Bound (ELBO) of the true data likelihood with respect to a variational distribution. However, freely enriching the family of variational distribution is challenging since the ELBO requires variational likelihood evaluations of the latent variables...

Likelihood-free inference via classification

Increasingly complex generative models are being used across disciplines as they allow for realistic characterization of data, but a common difficulty with them is the prohibitively large computational cost to evaluate the likelihood function and thus to perform likelihood-based statistical inference. A likelihood-free inference framework has emerged where the parameters are identified by findi...