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PyMC (formerly known as PyMC3) is a probabilistic programming language written in Python. It can be used for Bayesian statistical modeling and probabilistic machine learning. It can be used for Bayesian statistical modeling and probabilistic machine learning.
Bayesian hierarchical modelling is a statistical model written in multiple levels (hierarchical form) that estimates the parameters of the posterior distribution using the Bayesian method. [1] The sub-models combine to form the hierarchical model, and Bayes' theorem is used to integrate them with the observed data and account for all the ...
In Bayesian statistics, Markov chain Monte Carlo methods are typically used to calculate moments and credible intervals of posterior probability distributions. The use of MCMC methods makes it possible to compute large hierarchical models that require integrations over hundreds to thousands of unknown parameters.
The deviance information criterion (DIC) is a hierarchical modeling generalization of the Akaike information criterion (AIC). It is particularly useful in Bayesian model selection problems where the posterior distributions of the models have been obtained by Markov chain Monte Carlo (MCMC) simulation.
Just another Gibbs sampler (JAGS) is a program for simulation from Bayesian hierarchical models using Markov chain Monte Carlo (MCMC), developed by Martyn Plummer. JAGS has been employed for statistical work in many fields, for example ecology, management, and genetics.
Comparison of models, including model selection or model averaging; Preparation of the results for a particular audience; All these tasks are part of the Exploratory analysis of Bayesian models approach, and successfully performing them is central to the iterative and interactive modeling process. These tasks require both numerical and visual ...
Betancourt, Michael; Girolami, Mark (2015). "Hamiltonian Monte Carlo for Hierarchical Models". In Upadhyay, Satyanshu Kumar; et al. (eds.). Current Trends in Bayesian Methodology with Applications. CRC Press. pp. 79– 101. ISBN 978-1-4822-3511-1. Betancourt, Michael (2018). "A Conceptual Introduction to Hamiltonian Monte Carlo".
A Bayesian network (also known as a Bayes network, Bayes net, belief network, or decision network) is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). [1]