Likelihood-free inference in high dimensions with synthetic likelihood
نویسندگان
چکیده
منابع مشابه
Likelihood-Free Inference in High-Dimensional Models.
Methods that bypass analytical evaluations of the likelihood function have become an indispensable tool for statistical inference in many fields of science. These so-called likelihood-free methods rely on accepting and rejecting simulations based on summary statistics, which limits them to low-dimensional models for which the value of the likelihood is large enough to result in manageable accep...
متن کاملLikelihood 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...
متن کاملLikelihood-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...
متن کاملELFI: Engine for Likelihood Free Inference
The Engine for Likelihood-Free Inference (ELFI) is a Python software library for performing likelihood-free inference (LFI). ELFI provides a convenient syntax for specifying LFI models commonly composed of priors, simulators, summaries, distances and other custom operations. These can be implemented in a wide variety of languages. Separating the modelling task from the inference makes it possib...
متن کاملLikelihood inference
The essential role of the likelihood function in both Bayesian and non-Bayesian inference is described. Several topics related to the extension of likelihood-based methodology to more complex settings are reviewed, including modifications to profile likelihood, composite and pseudo-likelihoods, quasi-likelihood, semiparametric and non-parametric likelihoods, and empirical likelihood . 2010 Jo...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computational Statistics & Data Analysis
سال: 2018
ISSN: 0167-9473
DOI: 10.1016/j.csda.2018.07.008