Master Bayesian Inference through Practical Examples and ComputationWithout Advanced Mathematical Analysis Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practicefreeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. Youll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once youve mastered these techniques, youll constantly turn to this guide for the working PyMC code you need to jumpstart future projects. Coverage includes Learning the Bayesian state of mind and its practical implications Understanding how computers perform Bayesian inference Using the PyMC Python library to program Bayesian analyses Building and debugging models with PyMC Testing your models goodness of fit Opening the black box of the Markov Chain Monte Carlo algorithm to see how and why it works Leveraging the power of the Law of Large Numbers Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning Using loss functions to measure an estimates weaknesses based on your goals and desired outcomes Selecting appropriate priors and understanding how their influence changes with dataset size Overcoming the exploration versus exploitation deciding when pretty good is good enough Using Bayesian inference to improve A/B testing Solving data science problems when only small amounts of data are available Cameron Davidson-Pilon has worked in many areas of applied mathematics, from the evolutionary dynamics of genes and diseases to stochastic modeling of financial prices. His contributions to the open source community include lifelines, an implementation of survival analysis in Python. Educated at the University of Waterloo and at the Independent University of Moscow, he currently works with the online commerce leader Shopify.

zlib/no-category/Cameron Davidson-Pilon/Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference (Addison-Wesley Data & Analytics Series)_118338797.azw3

Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference (Addison-Wesley Data & Analytics) (Addison-Wesley Data & Analytics)

Bayesian methods for hackers : probabilistic programming and Bayesian methods

Вероятностное программирование на Python. Байесовский вывод и алгоритмы: 16+

Кэмерон Дэвидсон-Пайлон; [перевод с английского И. Пальти, К. Русецкий]

Дэвидсон-Пайлон, Кэмерон

Globe Fearon Educational Publishing

Addison-Wesley Professional

Longman Publishing

Cengage Gale

Питер

Addison Wesley data & analytics series, Addison-Wesley data and analytics series, New York State, 2016

United States, United States of America

Санкт-Петербург [и др.], Russia, 2020

Санкт-Петербург [и др.], Russia, 2019

1, 2015-10-02

1, US, 2015

Includes bibliographical references and index.

Фактическая дата выхода в свет - 2019
Библиогр. в конце гл.
Пер.: Davidson-Pilon, Cameron Bayesian methods for hackers 978-0133902839

РГБ

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Desccripción del edior: "Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice-freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You'll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you've mastered these techniques, you'll constantly turn to this guide for the working PyMC code you need to jumpstart future projects." (Amazon)

2024-06-27