Probabilistic modeling

graduate
bayesian modeling
python
This course provides the student with knowledge on methods for probabilistic modeling applied to inference problems.
Author

Marco A. Alsina

Objectives

  • State measurement problems from a probability perspective.
  • Recognize central aspects of Bayesian modeling.
  • Address simple inference problems through probabilistic models.
  • Address complex inference problems through Bayesian modeling.

Contents

Unit I: Basics

  1. Concepts and definitions
  2. Counting methods
  3. Conditional probability

Unit II: Environmental analysis

  1. Discrete and continous distributions
  2. Joint probability
  3. Binomial probability
  4. Modeling counts and measurements

Unit III: Environmental impact

  1. Monte Carlo Markov Chain simulations
  2. Bayesian linear regression
  3. Bayesian multiple regression

Resources

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