Applied Bayesian for Analytics (edX)

Applied Bayesian for Analytics (edX)
Course Auditing
Categories
Effort
Certification
Languages
Basic understanding of Statistics.
Misc

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Applied Bayesian for Analytics (edX)
Learn how to construct, fit, estimate and compute Bayesian statistical models with the help of OpenBUGS (freely available software). Bayesian Statistics is a captivating field and is used most prominently in data sciences. In this course we will learn about the foundation of Bayesian concepts, how it differs from Classical Statistics including among others Parametrizations, Priors, Likelihood, Monte Carlo methods and computing Bayesian models with the exploration of Multilevel modelling.

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This course is divided into two parts i.e. Theoretical and Empirical part of Bayesian Analytics. First three weeks cover the Theoretical part which includes how to form a prior, how to calculate a posterior and several other aspects. Rest of the weeks will cover the empirical part which explains how to compute Bayesian modelling. Completion of this course will provide you with an understanding of the Bayesian approach, the primary difference between Bayesian and Frequentist approaches and experience in data analyses.


What you'll learn

- Understand the necessary Bayesian concepts from practical point of view for better decision making.

- Learn Bayesian approach to estimate likely event outcomes, or probabilities using datasets.

- Gain “hands on” experience in creating and estimating Bayesian models using R and OPENBUGS.


Syllabus


Week 01: What is Bayesian Statistics and How it is different than Classical Statistics

Foundations of Bayesian Inference

Bayes theorem

Advantages of Bayesian models

Why Bayesian approach is so important in Analytics

Major densities and their applications


Week 02: Bayesian analysis of Simple Models

Likelihood theory and Estimation

Parametrizations and priors

Learning from binary models

Learning from Normal Distribution


Week 03: Monte Carlo Methods

Basics of Monte carol integration

Basics of Markov chain Monte Carlo

Gibs Sampling


Week 04: Computational Bayes

Examples of Bayesian Analytics

Introduction to R and OPENBUGS for Bayesian analysis


Week 05: Bayesian Linear Models

Context for Bayesian Regression Models

Normal Linear regression

Logistic regression


Week 06: Bayesian Hierarchical Models

Introduction to Multilevel models

Exchangeability

Computation in Hierarchical Models



MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Course Auditing
139.00 EUR
Basic understanding of Statistics.

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.