In this work, the authors analyze Bayesian multimodel inference (MMI) to address the problem of making predictions when multiple mathematical models of a biological system are available. MMI combines ...

Virtually all computations performed by the nervous system are subject to uncertainty and taking this into account is critical for making inferences about the outside world. For instance, imagine ...

Articulate the primary interpretations of probability theory and the role these interpretations play in Bayesian inference Use Bayesian inference to solve real-world statistics and data science ...

Dirichlet process (DP) priors are a popular choice for semiparametric Bayesian random effect models. The fact that the DP prior implies a non-zero mean for the random effect distribution creates an ...

This is a preview. Log in through your library . Abstract In this paper, we derive restrictions for Granger noncausality in MS-VAR models and show under what conditions a variable does not affect the ...