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- ArviZ (/ˈɑːrvɪz/ AR-vees) is a Python package for exploratory analysis of Bayesian models. When working with Bayesian models there are a series of related tasks that need to be addressed besides inference itself:
* Diagnoses of the quality of the inference, this is needed when using numerical methods such as Markov chain Monte Carlo techniques
* Model criticism, including evaluations of both model assumptions and model predictions
* 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 summaries. ArviZ offers data structures for manipulating data common in Bayesian analysis, like numerical samples from the posterior, prior predictive and posterior predictive distributions as well as observed data. Additionally, many numerical and visual diagnostics as well as plots are available. The ArviZ name is derived from reading "rvs" (the short form of random variates) as a word instead of spelling it and also using the particle "viz" usually used to abbreviate visualization. ArviZ is an open source project, developed by the community and is an affiliated project of . and it has been used to help interpret inference problems in several scientific domains, including astronomy, neuroscience, physics and statistics. (en)
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- 9442 (xsd:nonNegativeInteger)
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- ArviZ Development Team (en)
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- ArviZ (/ˈɑːrvɪz/ AR-vees) is a Python package for exploratory analysis of Bayesian models. When working with Bayesian models there are a series of related tasks that need to be addressed besides inference itself:
* Diagnoses of the quality of the inference, this is needed when using numerical methods such as Markov chain Monte Carlo techniques
* Model criticism, including evaluations of both model assumptions and model predictions
* Comparison of models, including model selection or model averaging
* Preparation of the results for a particular audience (en)
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