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Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference (Addison-Wesley Data & Analytics)
Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference (Addison-Wesley Data & Analytics)
Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference (Addison-Wesley Data & Analytics)
Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference (Addison-Wesley Data & Analytics)
Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference (Addison-Wesley Data & Analytics)
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Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference (Addison-Wesley Data & Analytics)
Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference (Addison-Wesley Data & Analytics)
Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference (Addison-Wesley Data & Analytics)
Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference (Addison-Wesley Data & Analytics)
Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference (Addison-Wesley Data & Analytics)
Bayesian methods of inference are deeply natural and extremely powerful. 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.
Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference (Addison-Wesley Data & Analytics)
Artigo n.º: 243021207

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

85% of respondents would recommend this to a friend

Artigo n.º: 243021207

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Bayesian methods of inference are deeply natural and extremely powerful. 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.
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O que se Destaca

Hands-On Approach
Utilizes practical coding examples to demystify Bayesian concepts, making it accessible to hackers and programmers looking to apply statistical methods in real-world scenarios.
Comprehensive Coverage
Covers both fundamental and advanced topics in probabilistic programming and Bayesian inference, catering to both beginners and experienced users seeking to enhance their statistical toolkit.
Community Focused
Promotes collaboration and discussion within the data science community, aiding readers in problem-solving and sharing insights to foster a deeper understanding of Bayesian methodologies.

Detalhes do produto

Shop Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference (Addison-Wesley Data & Analytics) online at a best price in Cabo Verde. 0133902838
  • Master Bayesian Inference through Practical Examples and Computation–Without Advanced Mathematical AnalysisBayesian 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.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 model’s “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 estimate’s 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” dilemma: 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 availableCameron 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.
Publisher Addison-Wesley Professional
Publication date 2 Oct. 2015
Edition 1st
Language English
Print length 250 pages
ISBN-10 0133902838
ISBN-13 978-0133902839
Item weight 382 g
Dimensions 17.78 x 1.45 x 23.19 cm

Quem Deverá Comprar?

Suitable For
  • Data Scientists

    Those looking to deepen their understanding of Bayesian methods in data analysis and predictive modeling will benefit significantly.

  • Statistical Programmers

    Users familiar with Python who want to apply Bayesian techniques through practical coding examples will find it invaluable.

  • Graduate Students

    Students in statistics or data science seeking a hands-on introduction to probabilistic programming will gain a solid foundation.

Not Suitable For
  • Absolute Beginners

    Individuals with no background in statistics or programming may find the material too complex and challenging to comprehend.

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Data Mining Editorial Review

Bayesian Methods For Hackers: Probabilistic Programming And Bayesian Inference (Addison-Wesley Data & Analytics) offers a comprehensive introduction to Bayesian inference, adeptly using the pymc library to simplify complex concepts with practical examples. The book's accessible chatty writing style makes understanding math enjoyable, and it encourages readers to experiment with code on their own. Although some readers noted discrepancies with library versions, many found it easy to adapt the examples to their Python environment. This 250-page volume, published by Addison-Wesley Professional, is ideal for those keen on enhancing their statistical programming skills, especially in Python.

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Prós

  • Accessible writing style makes complex concepts easy to grasp
  • Provides practical examples to reinforce learning
  • Encourages experimentation with code and real-world applications
  • Detailed exploration of Bayesian methods for learners
  • Great resources for users familiar with Python

Contras

  • Content is available freely online, which may influence buyers.

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Recursos e benefícios

  • Deeply natural and extremely powerful Bayesian methods of inference
  • Learn Bayesian inference from a computational perspective using PyMC
  • Illuminates Bayesian inference through probabilistic programming with NumPy, SciPy, and Matplotlib
  • Building and debugging models, testing model's “goodness of fit”
  • Overcoming exploration versus exploitation dilemma, using Bayesian Inference to improve A/B testing
  • Solving data science problems with small amounts of data

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