Discussion points for Bayesian inference

Using TPA for Bayesian inference — Discussion

Skilling (2007) previously identified that the number of steps required to reach a given set is Poisson distributed. Huber and Schott suggest making this special case central, recasting all computations as finding the mass of a distribution on a set. Additional contributions are a theoretical analysis, two general ways of reducing problems to the required form and a link to annealing. The resul...

Bayesian Computational Methods for Inference in Multiple Change-points Models

Multiple change-point models provide a exible and interpretable framework for representation of temporal heterogeneity in data. In addition to the locations of changepoints, these models typically involve parameters which specify the distributions of data between change-points and other quantities. However, the values of these parameters are usually unknown and need to be inferred from the data...

Bayesian Inference for Spatial Beta Generalized Linear Mixed Models

In some applications, the response variable assumes values in the unit interval. The standard linear regression model is not appropriate for modelling this type of data because the normality assumption is not met. Alternatively, the beta regression model has been introduced to analyze such observations. A beta distribution represents a flexible density family on (0, 1) interval that covers symm...

Bayesian Nonparametric and Parametric Inference

This paper reviews Bayesian Nonparametric methods and discusses how parametric predictive densities can be constructed using nonparametric ideas.

Discussion Points