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Fast inference in generalized linear models via expected log-likelihoods
Fast inference generalized linear models expected log-likelihoods
2013/6/14
Generalized linear models play an essential role in a wide variety of statistical applications. This paper discusses an approximation of the likelihood in these models that can greatly facilitate comp...
Approximate Inference for Observation Driven Time Series Models with Intractable Likelihoods
Observation Driven Time Series Models Approximate Bayesian Computation Asymptotic Con-sistency Markov Chain Monte Carlo
2013/4/28
In the following article we consider approximate Bayesian parameter inference for observation driven time series models. Such statistical models appear in a wide variety of applications, including eco...
Distributed Learning of Gaussian Graphical Models via Marginal Likelihoods
Distributed Learning Gaussian Graphical Models Marginal Likelihoods
2013/4/28
We consider distributed estimation of the inverse covariance matrix, also called the concentration matrix, in Gaussian graphical models. Traditional centralized estimation often requires iterative and...
Dirichlet Posterior Sampling with Truncated Multinomial Likelihoods
Dirichlet Posterior Sampling Multinomial Likelihoods
2012/9/18
This document considers the problem of drawing samples from posterior distributions formed under a Dirichlet prior and a truncated multinomial likelihood, by which we mean a Multi-nomial likelihood fu...
Mapping gravitational lensing of the CMB using local likelihoods
Cosmology and Extragalactic Astrophysics (astro-ph.CO) Applications (stat.AP)
2010/12/17
We present a new estimation method for mapping the gravitational lensing potential from observed CMB intensity and polarization fields. Our method uses Bayesian techniques to estimate the average curv...