Model choice plays an increasingly important role in statistics. From a Bayesian perspective
a crucial goal is to compute the marginal likelihood of the data for a given model. However …

Determining the marginal likelihood from a simulated posterior distribution is central to
Bayesian model selection but is computationally challenging. The often-used harmonic …

Structured additive regression models are perhaps the most commonly used class of models
in statistical applications. It includes, among others,(generalized) linear …

In Bayesian inference, the posterior distribution for parameters is given by л (0| y)∞ л (у| О)
л (0), where one's prior beliefs about the unknown parameters, as expressed through the …

SUMMARY Markov chain Monte Carlo (MCMC) integration methods enable the fitting of
models of virtually unlimited complexity, and as such have revolutionized the practice of …

PyMC: Bayesian Stochastic Modelling in Python - PMC Back to Top Skip to main content NIH
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The integrated likelihood (also called the marginal likelihood or the normalizing constant) is
a central quantity in Bayesian model selection and model averaging. It is defined as the …

In Bayesian inference, predictive distributions are typically in the form of samples generated
via Markov chain Monte Carlo or related algorithms. In this paper, we conduct a systematic …

We consider the problem of comparing parametric models using a Bayesian approach. A
new method of developing prior distributions for the model parameters is presented, called …

We review the across‐model simulation approach to computation for Bayesian model
determination, based on the reversible jump Markov chain Monte Carlo method …