# Introduction to Bayesian Analysis in Python [Video]

 Learn Understand the essentials Bayesian concepts from a practical point of view Learn how to build probabilistic models using the Python library PyMC3 Acquire the skills to sanity-check your models and modify them if necessary Implement parametric models for your generalized linear models Add structure to your models and get the advantages of hierarchical models Find out how different models can be used to answer different data analysis questions  Learn how to think probabilistically and unleash the power and flexibility of the Bayesian framework Bayesian statistics is an effective tool for solving some inference problems when the available sample is too small for more complex statistical analysis to be applied. This course teaches the main concepts of Bayesian data analysis. It focuses on how to effectively use PyMC3, a Python library for probabilistic programming, to perform Bayesian parameter estimation, model checking, and validation. The course introduces the framework of Bayesian Analysis. Complex mathematical theory will be sidestepped in favor of a more pragmatic approach, featuring computational methods implemented in the Python library PyMC3. We present several instances of analysis scenarios. All the codes of the course are uploaded on the Github repository: https://github.com/PacktPublishing/-Introduction-to-Bayesian-Analysis-in-Python Style and Approach The user is expected to know basic Python programming. Knowledge of scientific Python packages such as NumPy, SciPy, Matplotlib, Seaborn, and Pandas is a plus but not mandatory. However, a basic understanding of probability is necessary to grasp the fundamentals of the Bayesian framework. Familiarity with the notion of random variables and distributions will enable you to follow along with the course. Simplify the Bayes process to solve complex statistical problems using Python Tutorial guide that will take the you through the journey to Bayesian analysis with the help of sample problems and practice exercises Learn how and when to use Bayesian analysis in your applications with this guide 1 hours 10 minutes 9781788997010 28 Dec 2018
 Choosing Priors Loss Functions Model Evaluation
 Choosing Priors Constructing a Loss Function PyMC3 Implementation Training and Results