University of California, Santa Cruz
Bayesian Statistics Specialization
University of California, Santa Cruz

Bayesian Statistics Specialization

Bayesian Statistics for Modeling and Prediction. Learn the foundations and practice your data analysis skills.

Taught in English

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Herbert Lee
Matthew Heiner
Abel Rodriguez

Instructors: Herbert Lee

11,474 already enrolled

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Specialization - 5 course series

Get in-depth knowledge of a subject

4.6

(288 reviews)

Intermediate level

Recommended experience

2 months at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Bayesian Inference

  • Time Series Forecasting

  • Hierarchical Modeling

Details to know

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Specialization - 5 course series

Get in-depth knowledge of a subject

4.6

(288 reviews)

Intermediate level

Recommended experience

2 months at 10 hours a week
Flexible schedule
Learn at your own pace

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Specialization - 5 course series

Bayesian Statistics: From Concept to Data Analysis

Course 111 hours4.6 (3,135 ratings)

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.

Skills you'll gain

Category: Forecasting
Category: Bayesian Statistics
Category: Time Series
Category: Dynamic Linear Modeling
Category: R Programming

Bayesian Statistics: Techniques and Models

Course 229 hours4.8 (473 ratings)

What you'll learn

  • Efficiently and effectively communicate the results of data analysis.

  • Use statistical modeling results to draw scientific conclusions.

  • Extend basic statistical models to account for correlated observations using hierarchical models.

Skills you'll gain

Category: Gibbs Sampling
Category: Bayesian Statistics
Category: Bayesian Inference
Category: R Programming

Bayesian Statistics: Mixture Models

Course 321 hours4.6 (48 ratings)

What you'll learn

  • Explain the basic principles behind the algorithm for fitting a mixture model.

  • Compute the expectation and variance of a mixture distribution.

  • Use mixture models to solve classification and clustering problems, and to provide density estimates.

Bayesian Statistics: Time Series Analysis

Course 422 hours4.5 (11 ratings)

What you'll learn

  • Build models that describe temporal dependencies.

  • Use R for analysis and forecasting of times series.

  • Explain stationary time series processes.

Skills you'll gain

Category: Statistics
Category: Bayesian Statistics
Category: Bayesian Inference
Category: R Programming

What you'll learn

  • Demonstrate a wide range of skills and knowledge in Bayesian statistics.

  • Explain essential concepts in Bayesian statistics.

  • Apply what you know to real-world data.

Skills you'll gain

Category: Markov Model
Category: Bayesian Statistics
Category: Mixture Model
Category: R Programming

Instructors

Herbert Lee
University of California, Santa Cruz
1 Course147,896 learners
Matthew Heiner
University of California, Santa Cruz
1 Course54,749 learners

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