Binomial-Beta Hierarchical Models for Ecological Inference

Binomial-Beta Hierarchical Models for Ecological Inference

insights from the literature on hierarchical models based on Markov chain Monte Carlo algorithms and King’s ecological inference model. The new approach reveals some features of the data that King’s approach does not, can be easily generalized to more complicated problems such as general R × C tables, allows the data analyst to adjust for covariates, and provides a formal evaluation of the sign...

Binomial - Beta Hierarchical Models for EcologicalInference 1

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...

Monte Carlo Markov Chain Exact Inference for Binomial Regression Models

Current methods for conducting exact inference for logistic regression are not capable of handling large data sets due to memory constraints caused by storing large networks. We provide and implement an algorithm which is capable of conducting (approximate) exact inference for large data sets. Various application fields, such as genetic epidemiology, in which logistic regression models are fit ...

Fully Bayesian inference for neural models with negative-binomial spiking

Characterizing the information carried by neural populations in the brain requires accurate statistical models of neural spike responses. The negative-binomial distribution provides a convenient model for over-dispersed spike counts, that is, responses with greater-than-Poisson variability. Here we describe a powerful data-augmentation framework for fully Bayesian inference in neural models wit...