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Bayesian Statistics
  • Level Professional
  • المدة
  • الطبع بواسطة Duke University
  • Offered by

عن

This Specialization is intended for all learners seeking to develop proficiency in statistics, Bayesian statistics, Bayesian inference, R programming, and much more. Through four complete courses (From Concept to Data Analysis; Techniques and Models; Mixture Models; Time Series Analysis) and a culminating project, you will cover Bayesian methods — such as conjugate models, MCMC, mixture models, and dynamic linear modeling — which will provide you with the skills necessary to perform analysis, engage in forecasting, and create statistical models using real-world data.

الوحدات

Overview

    • 1 Videos
    • 4 Readings
    • 1 Discussion
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1 Discussions

  • Introduce Yourself

1 Videos

  • Introduction to Statistics with R

4 Readings

  • About Statistics with R Specialization
  • About Bayesian Statistics
  • Pre-requisite Knowledge
  • Special Thanks

Introduction to Bayesian Statistics

    • 1 Videos
    • 1 Readings
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1 Videos

  • The Basics of Bayesian Statistics

1 Readings

  • Module Learning Objectives

Bayes' Rule

    • 4 Videos
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4 Videos

  • Conditional Probabilities and Bayes' Rule
  • Bayes' Rule and Diagnostic Testing
  • Bayes Updating
  • Bayesian vs. frequentist definitions of probability

Inference for a Proportion

    • 3 Videos
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3 Videos

  • Inference for a Proportion: Frequentist Approach
  • Inference for a Proportion: Bayesian Approach
  • Effect of Sample Size on the Posterior

Frequentist vs. Bayesian Inference

    • 1 Videos
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1 Videos

  • Frequentist vs. Bayesian Inference

Learning R

    • 3 Readings
    • 1 Assignment
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1 Assignment

  • Week 1 Lab

3 Readings

  • About Lab Choices
  • Week 1 Lab Instructions (RStudio)
  • Week 1 Lab Instructions (RStudio Cloud)

Strengthen Your Understanding

    • 2 Assignment
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2 Assignment

  • Week 1 Practice Quiz
  • Week 1 Quiz

Introduction to Bayesian Inference

    • 1 Videos
    • 1 Readings
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1 Videos

  • Bayesian Inference

1 Readings

  • Module Learning Objectives

Continuous Variables and Eliciting Probability Distributions

    • 3 Videos
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3 Videos

  • From the Discrete to the Continuous
  • Elicitation
  • Conjugacy

Three Conjugate Families

    • 3 Videos
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3 Videos

  • Inference on a Binomial Proportion
  • The Gamma-Poisson Conjugate Families
  • The Normal-Normal Conjugate Families

Credible Intervals and Predictive Inference

    • 3 Videos
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3 Videos

  • Non-Conjugate Priors
  • Credible Intervals
  • Predictive Inference

Learning R

    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Week 2 Lab

2 Readings

  • Week 2 Lab Instructions (RStudio)
  • Week 1 Lab Instructions (RStudio Cloud)

Strengthen Your Understanding

    • 2 Assignment
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2 Assignment

  • Week 2 Practice Quiz
  • Week 2 Quiz

Decision Making

    • 1 Videos
    • 1 Readings
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1 Videos

  • Decision making

1 Readings

  • Module Learning Objectives

Losses and Decision Making

    • 4 Videos
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4 Videos

  • Losses and decision making
  • Working with loss functions
  • Minimizing expected loss for hypothesis testing
  • Posterior probabilities of hypotheses and Bayes factors

Inference for Normal Data

    • 5 Videos
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5 Videos

  • The Normal-Gamma Conjugate Family
  • Inference via Monte Carlo Sampling
  • Predictive Distributions and Prior Choice
  • Reference Priors
  • Mixtures of Conjugate Priors and MCMC

Comparing Normal Means and Testing Hypotheses

    • 4 Videos
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4 Videos

  • Hypothesis Testing: Normal Mean with Known Variance
  • Comparing Two Paired Means Using Bayes' Factors
  • Comparing Two Independent Means: Hypothesis Testing
  • Comparing Two Independent Means: What to Report?

Learning R

    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Week 3 Lab

2 Readings

  • Week 3 Lab Instructions (RStudio)
  • Week 3 Lab Instructions (RStudio Cloud)

Strengthen Your Understanding

    • 2 Assignment
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2 Assignment

  • Week 3 Practice Quiz
  • Week 3 Quiz

Introducing Bayesian Regression

    • 1 Videos
    • 1 Readings
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1 Videos

  • Bayesian regression

1 Readings

  • Module Learning Objectives

Simple and Multiple Bayesian Regressions

    • 3 Videos
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3 Videos

  • Bayesian simple linear regression
  • Checking for outliers
  • Bayesian multiple regression

Bayesian Model Uncertainty and Model Averaging

    • 3 Videos
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3 Videos

  • Model selection criteria
  • Bayesian model uncertainty
  • Bayesian model averaging

Markov Chain Monte Carlo

    • 4 Videos
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4 Videos

  • Stochastic exploration
  • Priors for Bayesian model uncertainty
  • R demo: crime and punishment
  • Decisions under model uncertainty

Learning R

    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Week 4 Lab

2 Readings

  • Week 4 Lab Instructions (RStudio Cloud)
  • Week 4 Lab Instructions (RStudio Cloud)

Strengthen Your Understanding

    • 2 Assignment
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2 Assignment

  • Week 4 Practice Quiz
  • Week 4 Quiz

Interviews

    • 3 Videos
    • 1 Readings
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3 Videos

  • Bayesian inference: a talk with Jim Berger
  • Bayesian methods and big data: a talk with David Dunson
  • Bayesian methods in biostatistics and public health: a talk with Amy Herring

1 Readings

  • About this module

Peer Review Project

    • 1 Readings
    • 1 PeerReview
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1 Peer Review

  • Data Analysis Project

1 Readings

  • Project information

Auto Summary

Explore Bayesian Statistics in this comprehensive course focused on updating inferences with accumulating evidence. Taught by Coursera, it delves into Bayes’ rule, model building, and practical applications using R. Ideal for those with prior knowledge in probability, inferential statistics, and regression, this professional-level course offers flexible subscription options. Perfect for learners aiming to master Bayesian methods in Big Data and Analytics.

Mine Çetinkaya-Rundel
Instructors

Mine Çetinkaya-Rundel

David Banks
Instructors

David Banks

Colin Rundel
Instructors

Colin Rundel

Merlise A Clyde
Instructors

Merlise A Clyde