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Published inJan 2024
Reading LevelExpert
PublisherPackt
ISBN-139781805127161
Edition3rd Edition
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Osvaldo Martin
Osvaldo Martin
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Osvaldo Martin

Osvaldo Martin is a researcher at CONICET, in Argentina. He has experience using Markov Chain Monte Carlo methods to simulate molecules and perform Bayesian inference. He loves to use Python to solve data analysis problems. He is especially motivated by the development and implementation of software tools for Bayesian statistics and probabilistic modeling. He is an open-source developer, and he contributes to Python libraries like PyMC, ArviZ and Bambi among others. He is interested in all aspects of the Bayesian workflow, including numerical methods for inference, diagnosis of sampling, evaluation and criticism of models, comparison of models and presentation of results.
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2.7 InferenceData

InferenceData is a rich container for the results of Bayesian inference. A modern Bayesian analysis potentially generates many sets of data including posterior samples and posterior predictive samples. But we also have observed data, samples from the prior, and even statistics generated by the sampler. All this data, and more, can be stored in an InferenceData object. To help keep all this information organized, each one of these sets of data has its own group. For instance, the posterior samples are stored in the posterior group. The observed data is stored in the observed_data group.

Figure 2.18 shows an HTML representation of the InferenceData for model_g. We can see 4 groups: posterior, posterior_predictive, sample_stats, and observed_data. All of them are collapsed except for the posterior group. We can see we have two coordinates chain and draw of dimensions 4 and 1000 respectively. We also have 2 variables μ and σ.

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Figure 2.18: InferenceData...

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Bayesian Analysis with Python - Third Edition
Published in: Jan 2024Publisher: PacktISBN-13: 9781805127161

Author (1)

author image
Osvaldo Martin

Osvaldo Martin is a researcher at CONICET, in Argentina. He has experience using Markov Chain Monte Carlo methods to simulate molecules and perform Bayesian inference. He loves to use Python to solve data analysis problems. He is especially motivated by the development and implementation of software tools for Bayesian statistics and probabilistic modeling. He is an open-source developer, and he contributes to Python libraries like PyMC, ArviZ and Bambi among others. He is interested in all aspects of the Bayesian workflow, including numerical methods for inference, diagnosis of sampling, evaluation and criticism of models, comparison of models and presentation of results.
Read more about Osvaldo Martin