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  2. PyMC - Wikipedia

    en.wikipedia.org/wiki/PyMC

    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.

  3. Bayesian hierarchical modeling - Wikipedia

    en.wikipedia.org/wiki/Bayesian_hierarchical_modeling

    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 ...

  4. Markov chain Monte Carlo - Wikipedia

    en.wikipedia.org/wiki/Markov_chain_Monte_Carlo

    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.

  5. Deviance information criterion - Wikipedia

    en.wikipedia.org/wiki/Deviance_information_criterion

    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.

  6. Just another Gibbs sampler - Wikipedia

    en.wikipedia.org/wiki/Just_another_Gibbs_sampler

    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.

  7. ArviZ - Wikipedia

    en.wikipedia.org/wiki/ArviZ

    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 ...

  8. Hamiltonian Monte Carlo - Wikipedia

    en.wikipedia.org/wiki/Hamiltonian_Monte_Carlo

    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".

  9. Bayesian network - Wikipedia

    en.wikipedia.org/wiki/Bayesian_network

    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]