University of California, Santa Cruz
Bayesian Statistics: From Concept to Data Analysis
University of California, Santa Cruz

Bayesian Statistics: From Concept to Data Analysis

This course is part of Bayesian Statistics Specialization

Taught in English

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Herbert Lee

Instructor: Herbert Lee

148,462 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.6

(3,140 reviews)

|

91%

Intermediate level
Some related experience required
11 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • Describe & apply the Bayesian approach to statistics.

  • Explain the key differences between Bayesian and Frequentist approaches.

  • Master the basics of the R computing environment.

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Assessments

18 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.6

(3,140 reviews)

|

91%

Intermediate level
Some related experience required
11 hours (approximately)
Flexible schedule
Learn at your own pace

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This course is part of the Bayesian Statistics Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course

In this module, we review the basics of probability and Bayes’ theorem. In Lesson 1, we introduce the different paradigms or definitions of probability and discuss why probability provides a coherent framework for dealing with uncertainty. In Lesson 2, we review the rules of conditional probability and introduce Bayes’ theorem. Lesson 3 reviews common probability distributions for discrete and continuous random variables.

What's included

8 videos4 readings5 quizzes1 discussion prompt

This module introduces concepts of statistical inference from both frequentist and Bayesian perspectives. Lesson 4 takes the frequentist view, demonstrating maximum likelihood estimation and confidence intervals for binomial data. Lesson 5 introduces the fundamentals of Bayesian inference. Beginning with a binomial likelihood and prior probabilities for simple hypotheses, you will learn how to use Bayes’ theorem to update the prior with data to obtain posterior probabilities. This framework is extended with the continuous version of Bayes theorem to estimate continuous model parameters, and calculate posterior probabilities and credible intervals.

What's included

11 videos5 readings4 quizzes1 discussion prompt

In this module, you will learn methods for selecting prior distributions and building models for discrete data. Lesson 6 introduces prior selection and predictive distributions as a means of evaluating priors. Lesson 7 demonstrates Bayesian analysis of Bernoulli data and introduces the computationally convenient concept of conjugate priors. Lesson 8 builds a conjugate model for Poisson data and discusses strategies for selection of prior hyperparameters.

What's included

9 videos2 readings4 quizzes1 discussion prompt

This module covers conjugate and objective Bayesian analysis for continuous data. Lesson 9 presents the conjugate model for exponentially distributed data. Lesson 10 discusses models for normally distributed data, which play a central role in statistics. In Lesson 11, we return to prior selection and discuss ‘objective’ or ‘non-informative’ priors. Lesson 12 presents Bayesian linear regression with non-informative priors, which yield results comparable to those of classical regression.

What's included

9 videos5 readings5 quizzes1 discussion prompt

Instructor

Instructor ratings
4.6 (498 ratings)
Herbert Lee
University of California, Santa Cruz
1 Course148,462 learners

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Recommended if you're interested in Probability and Statistics

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