DeepAI AI Chat
Log In Sign Up

General dependence structures for some models based on exponential families with quadratic variance functions

by   Luis Nieto-Barajas, et al.

We describe a procedure to introduce general dependence structures on a set of random variables. These include order-q moving average-type structures, as well as seasonal, periodic and spatial dependences. The invariant marginal distribution can be in any family that is conjugate to an exponential family with quadratic variance functions. Dependence is induced via latent variables whose conditional distribution mirrors the sampling distribution in a Bayesian conjugate analysis of such exponential families. We obtain strict stationarity as a special case.


page 1

page 2

page 3

page 4


Dependence on a collection of Poisson random variables

We propose two novel ways of introducing dependence among Poisson counts...

Convolutional Deep Exponential Families

We describe convolutional deep exponential families (CDEFs) in this pape...

Bayesian Geostatistical Modeling for Discrete-Valued Processes

We introduce a flexible and scalable class of Bayesian geostatistical mo...

Estimating a regression function in exponential families by model selection

Let X_1=(W_1,Y_1),…,X_n=(W_n,Y_n) be n pairs of independent random varia...

Mixture decompositions of exponential families using a decomposition of their sample spaces

We study the problem of finding the smallest m such that every element o...

A Perfect Sampling Method for Exponential Family Random Graph Models

Generation of deviates from random graph models with non-trivial edge de...

Wishart laws and variance function on homogeneous cones

We present a systematic study of Riesz measures and their natural expone...