Hi, I want to implement Bayesian inference on a continuous mixture model, Poisson-Beta model, which can be written as follow formula: p|\alpha,\beta\sim Beta(\alpha,\beta) y|\mu,p \sim Poisson(\mu\cdot p) where \alpha, \beta, \mu are the inferred parameters. To obtain the posterior distribution…

The distributions shouldn’t match because in your creation of the array data, the values of a_real and b_real are fixed instead of sampled. Thus, these will have much lower dispersion (and a different distribution) than the outputs sampled under hierarchical_model. For an apples-to-apples compariso…

How to infer parameters for a continuous mixture model: Poisson-Beta model

Questions v5

Luo November 24, 2022, 6:57am 3

Thank you so much for your time and help! The code works perfectly！

Topic		Replies	Views
Struggling with Beta mixture models Questions	3	1874	February 2, 2019
How to improve fit of Poisson Mixture Model in PyMC3? Questions	20	5074	May 24, 2018
Categorical model with continuous dependent variable Questions	10	2977	February 5, 2018
Mixture of two beta geometric with beta shape > 1 Questions	6	1198	January 18, 2018
Continuous Distribution Alternative to Poisson to take advantage of NUTS Questions	1	798	January 4, 2018

How to infer parameters for a continuous mixture model: Poisson-Beta model

Related topics