Master Bayesian Inference through Practical Examples and Computation-Without Advanced Mathematical Analysis Bayesian Methods of inference are deeply natural and extremely powerful.
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.
Once you've mastered these techniques, you'll constantly turn to this guide for the working PyMC code you need to jumpstart future projects.
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.
Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback.
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.
Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention.
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.
Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice-freeing you to get results using computing power.
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.
Master Bayesian Inference through Practical Examples and Computation-Without Advanced Mathematical Analysis Bayesian Methods of inference are deeply natural and extremely powerful
