# Bayesian Statistics

University of California, Santa Cruz

This Specialization in Bayesian Statistics is designed to equip learners with proficiency in Bayesian methods, Bayesian inference, R programming, and more. It comprises four complete courses, including "From Concept to Data Analysis," "Techniques and Models," "Mixture Models," and "Time Series Analysis," along with a culminating project. Through engaging in-depth study, students will delve into essential topics such as Bayesian inference, time series forecasting, and hierarchical modeling.

Throughout the program, participants will develop a strong foundation in statistics and learn to apply Bayesian methods to real-world data. The courses will cover topics such as conjugate models, Markov chain Monte Carlo (MCMC), mixture models, and dynamic linear modeling. Learners will also gain practical experience in analyzing data, forecasting, and creating statistical models.

• Gain proficiency in Bayesian statistics and R programming
• Learn to apply Bayesian inference and time series forecasting
• Develop skills in hierarchical modeling and statistical modeling
• Complete a capstone project applying Bayesian statistics to real-world data

Certificate Available ✔

##### Course Modules

This Specialization comprises four comprehensive courses: "From Concept to Data Analysis," "Techniques and Models," "Mixture Models," and "Time Series Analysis," along with a culminating capstone project.

#### Bayesian Statistics: From Concept to Data Analysis

Describe & apply the Bayesian approach to statistics. Explain the key differences between Bayesian and Frequentist approaches. Master the basics of the R computing environment.

#### Bayesian Statistics: Techniques and Models

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.

#### Bayesian Statistics: Mixture Models

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

Build models that describe temporal dependencies. Use R for analysis and forecasting of times series. Explain stationary time series processes.

#### Bayesian Statistics: Capstone Project

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.

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