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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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6.5 Distributional models

We saw earlier that we can use linear models for parameters other than the mean (or location parameter). For example, we can use a linear model for the mean and a linear model for the standard deviation of a Gaussian distribution. These models are usually called distributional models. The syntax for distributional models is very similar; we just need to add a line for the auxiliary parameters we want to model. For instance, σ for a Gaussian, or α for a NegativeBinomial.

Let’s now reproduce an example from Chapter 4, the babies example:

Code 6.16

formula = bmb.Formula( 
    "length ∼ np.sqrt(month)", 
    "sigma ∼ month" 

model_dis = bmb.Model(formula, babies) 
idata_dis = model_dis.fit()

Figure 6.9 shows the posterior distribution values of sigma for model_dis (varying sigma) and for a model with constant sigma. We can see...

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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