Discrete parametric graphical models with a Dirichlet type priors

01/15/2023
by   Bartosz Kołodziejek, et al.
0

We introduce two discrete parametric graphical models on a finite decomposable graph. They are generalizations of negative multinomial and multinomial distributions. Their conjugate priors are also discussed, which generalize Dirichlet distribution and inverted Dirichlet distribution. Many classical properties of these distributions are generalized, in particular, these new models have explicit normalizing constants and they are graph Markov.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/30/2023

Random Discrete Probability Measures Based on Negative Binomial Process

An important functional of Poisson random measure is the negative binomi...
research
07/09/2023

Bayesian estimation of the Kullback-Leibler divergence for categorical sytems using mixtures of Dirichlet priors

In many applications in biology, engineering and economics, identifying ...
research
10/04/2018

Markov Properties of Discrete Determinantal Point Processes

Determinantal point processes (DPPs) are probabilistic models for repuls...
research
06/18/2021

Systemic Infinitesimal Over-dispersion on General Stochastic Graphical Models

Stochastic models of interacting populations have crucial roles in scien...
research
08/17/2017

Auxiliary Variables for Multi-Dirichlet Priors

Bayesian models that mix multiple Dirichlet prior parameters, called Mul...
research
06/01/2012

OpenGM: A C++ Library for Discrete Graphical Models

OpenGM is a C++ template library for defining discrete graphical models ...
research
03/06/2023

The Matrix-variate Dirichlet Averages and Its Applications

This paper is about Dirichlet averages in the matrix-variate case or ave...

Please sign up or login with your details

Forgot password? Click here to reset