BAP3

2024-02-08 23:44:46

The third version of Bayesian Evaluation with Python is an introduction to the principle ideas of utilized Bayesian
inference and its sensible implementation in Python utilizing state-of-the-art libraries like PyMC, ArviZ,
Bambi, PyMC-BART, PreliZ, and Kulprit.

By the tip of this e book, you’ll possess a practical understanding of probabilistic modeling, enabling you
to design and implement Bayesian fashions on your knowledge science challenges. And you’ll well-prepared to delve
into extra superior materials if the necessity arises.

Desk of Contents

Chapter 1: Considering Probabilistically
Chapter 2: Programming Probabilistically
Chapter 3: Hierarchical Fashions
Chapter 4: Modeling with Strains
Chapter 5: Evaluating Fashions
Chapter 6: Modeling with Bambi
Chapter 7: Combination Fashions
Chapter 8: Gaussian Processes
Chapter 9: Bayesian Additive Regression Bushes
Chapter 10: Inference Engines
Chapter 11: The place to Go Subsequent

Get the Ebook

You may get a replica of the e book from:

Code

All of the code used within the e book, together with code to generate lots of the photographs is obtainable at
GitHub

Errors

In the event you discover some an error within the e book, please fill an issue within the e book’s GitHub repository

Quotation

In the event you use this e book in your personal work, please cite it utilizing:
Martin Osvaldo A, Bayesian Evaluation with Python. Packt Publishing. 2024. ISBN 978-1-80512-716-1

Right here is the quotation in BibTeX format

            @e book{martin_bap_2024,
                title = {Bayesian {Evaluation} with {Python}: {A} {Sensible} {Information} to probabilistic modeling, third {Version}},
                isbn = {978-1-80512-716-1},
                shorttitle = {Bayesian {Evaluation} with {Python}},
                language = {English},
                writer = {Packt Publishing},
                creator = {Martin, Osvaldo A},
                month = feb,
                12 months = {2024},
            }

Donations

This e book is barely attainable due to open-source contributors.
In the event you finds this instruments helpful you possibly can donate to ArviZ
or PyMC to assist maintain their ongoing growth..

If as an alternative, you wish to donate on to me, the creator, please do it at ko-fi

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