Probabilistic Programming And Bayesian Methods For Hackers By Cameron Davidson Pilon Pdf

Bayesian Methods for Hackers

Master Bayesian Inference through Practical Examples and Computation–Without 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 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 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.

Bayesian Methods for Hackers

Probabilistic Programming and Bayesian Methods

Prologue: Why we do it. -- Chapter 1: Introduction to Bayesian Methods Introduction to the philosophy and practice of Bayesian methods and answering the question "What is probabilistic programming?" Examples include: Inferring human behaviour changes from text message rates -- Chapter 2: A little more on PyMC We explore modeling Bayesian problems using Python's PyMC library through examples. How do we create Bayesian models? Examples include: Detecting the frequency of cheating students, while avoiding liars. Calculating probabilities of the Challenger space-shuttle disaster. -- Chapter 3: Opening the Black Box of MCMC We discuss how MCMC operates and diagnostic tools. Examples include: Bayesian clustering with mixture models -- Chapter 4: The Greatest Theorem Never Told We explore an incredibly useful, and dangerous, theorem: The Law of Large Numbers. Examples include: Exploring a Kaggle dataset and the pitfalls of naive analysis How to sort Reddit comments from best to worst (not as easy as you think) -- Chapter 5: Would you rather loss an arm or a leg? The introduction of Loss functions and their (awesome) use in Bayesian methods. Examples include: Solving the Price is Right's Showdown Optimizing financial predictions Winning solution to the Kaggle Dark World's competition. -- Chapter 6: Getting our prior-ities straight Probably the most important chapter. We draw on expert opinions to answer questions. Examples include: Multi-Armed Bandits and the Bayesian Bandit solution. what is the relationship between data sample size and prior? estimating financial unknowns using expert priors We explore useful tips to be objective in analysis, and common pitfalls of priors. -- Chapter X1: Bayesian Markov Models -- Chapter X2: Bayesian methods in Machine Learning We explore how to resolve the overfitting problem plus popular ML methods. Also included are probablistic explainations of Ridge Regression and LASSO Regression. Bayesian spam filtering plus how to defeat Bayesian spam filtering Tim Saliman's winning solution to Kaggle's Don't Overfit problem -- Chapter X3: More PyMC Hackery We explore the gritty details of PyMC. Examples include: Analysis on real-time GitHub repo stars and forks. -- Chapter X4: Troubleshooting and debugging.