[Coursera] Bayesian Statistics Specialization

• Bayesian Statistics
• Data Science
• R Programming
• Data Analysis
• Statistics
• Bayesian Inference
• Gibbs Sampling
• Markov Model
• Mixture Model
• Forecasting
• Dynamic Linear Modeling
• Time Series

Description

Bayesian Statistics Specialization is a Bayesian statistics training package for forecasting and modeling published by Coursera Academy. In this training set, you will be introduced to the basics of Bayesian statistics and will strengthen your data analysis skills. This educational package includes various and scattered topics such as statistics, Bayesian statistics, Bayesian inference, R programming language, etc., and is a complete and comprehensive educational collection. This set consists of four separate courses and a completely practical project, and during its training process, different Bayesian statistical methods such as ex-conjugate distribution, Monte Carlo Markov chain, mixture models, dynamic linear modeling. And … you will learn. All taught methods will be used in performing professional statistical analysis, building various information models and statistical forecasting.

What you will learn in Bayesian Statistics Specialization

• Bayesian inference
• Time Series Forecasting
• Bayesian hierarchical modeling
• Data Science
• R programming language
• Analysis and analysis of information
• Bayesian statistics and statistics and its various methods
• Gibbs sampling
• Markov model
• Mixture Model
• Forecasting
• Dynamic linear modeling
• Time Series

Course Specifications

Publisher: Coursera
Instructors: Herbert Lee, Matthew Heiner, Abel Rodriguez, Raquel Prado and Jizhou Kang
Language: English
Level: Intermediate
institution/university: University of California, Santa Cruz
Number of Courses: 5
Duration: Approximately 6 months to complete – Suggested pace of 4 hours/week

Size: 3.64 GB

Download Now

https://www.coursera.org/specializations/bayesian-statistics.