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Home » Technology » AI / Machine Learning » Bayesian Analysis with Python

Course Skill Level:

Foundational to Intermediate

Course Duration:

2 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:

    BAYANPYL21E09

Who should attend & recommended skills:

Those with basic Python and Linux skills looking to learn Bayesian modeling with PyMC3 & exploratory analysis of Bayesian models with ArviZ.

Who should attend & recommended skills

  • This course is geared for those who want to know Bayesian modeling with PyMC3 and exploratory analysis of Bayesian models with ArviZ.
  • Skill-level: Foundation-level Python Machine Learning skills for Intermediate skilled team members. This is not a basic class.
  • Python: Basic to Intermediate (1-5 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su

About this course

Bayesian Analysis with Python is an introduction to the main concepts of applied Bayesian inference and its practical implementation in Python using PyMC3, a state-of-the-art probabilistic programming library, and ArviZ, a new library for exploratory analysis of Bayesian models. The main concepts of Bayesian statistics are covered using a practical and computational approach. Synthetic and real data sets are used to introduce several types of models, such as generalized linear models for regression and classification, mixture models, hierarchical models, and Gaussian processes, among others. By the end of the course, you will have a working knowledge of probabilistic modeling and you will be able to design and implement Bayesian models for your own data science problems. After reading the course you will be better prepared to delve into more advanced material or specialized statistical modeling if you need to.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Bayesian Analysis with Python instructor, participants will learn about and explore:
  • A step-by-step guide to conduct Bayesian data analyses using PyMC3 and ArviZ
  • A modern, practical and computational approach to Bayesian statistical modeling
  • A tutorial for Bayesian analysis and best practices with the help of sample problems and practice exercises.
  • Building probabilistic models using the Python library PyMC3
  • Analyzing probabilistic models with the help of ArviZ
  • Acquiring the skills required to sanity check models and modify them if necessary
  • Understanding the advantages and caveats of hierarchical models
  • Finding out how different models can be used to answer different data analysis questions
  • Comparing models and choose between alternative ones
  • Discovering how different models are unified from a probabilistic perspective
  • Thinking probabilistically and benefit from the flexibility of the Bayesian framework

Course breakdown / modules

  • Statistics, models, and this course's approach
  • Probability theory
  • Single-parameter inference
  • Communicating a Bayesian analysis
  • Posterior predictive checks

  • Probabilistic programming
  • PyMC3 primer
  • Summarizing the posterior
  • Gaussians all the way down
  • Groups comparison
  • Hierarchical models

  • Simple linear regression
  • Robust linear regression
  • Hierarchical linear regression
  • Polynomial regression
  • Multiple linear regression
  • Variable variance

  • Generalized linear models
  • Logistic regression
  • Multiple logistic regression
  • Poisson regression
  • Robust logistic regression
  • The GLM module

  • Posterior predictive checks
  • Occam's razor – simplicity and accuracy
  • Information criteria
  • Bayes factors
  • Regularizing priors
  • WAIC in depth

  • Finite mixture models
  • Non-finite mixture model
  • Continuous mixtures

  • Linear models and non-linear data
  • Modeling functions
  • Gaussian process regression
  • Regression with spatial autocorrelation
  • Gaussian process classification
  • Cox processes

  • Non-Markovian methods
  • Markovian methods
  • Diagnosing the samples
  • Statistics, models, and this course's approach
  • Probability theory
  • Single-parameter inference
  • Communicating a Bayesian analysis
  • Posterior predictive checks

  • Probabilistic programming
  • PyMC3 primer
  • Summarizing the posterior
  • Gaussians all the way down
  • Groups comparison
  • Hierarchical models

  • Simple linear regression
  • Robust linear regression
  • Hierarchical linear regression
  • Polynomial regression
  • Multiple linear regression
  • Variable variance

  • Generalized linear models
  • Logistic regression
  • Multiple logistic regression
  • Poisson regression
  • Robust logistic regression
  • The GLM module

  • Posterior predictive checks
  • Occam's razor – simplicity and accuracy
  • Information criteria
  • Bayes factors
  • Regularizing priors
  • WAIC in depth

  • Finite mixture models
  • Non-finite mixture model
  • Continuous mixtures

  • Linear models and non-linear data
  • Modeling functions
  • Gaussian process regression
  • Regression with spatial autocorrelation
  • Gaussian process classification
  • Cox processes

  • Non-Markovian methods
  • Markovian methods
  • Diagnosing the samples

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