# Bayesian Methods: Bayesian Concepts & Core Components

Bayesian statistics    |    Intermediate
• 11 Videos | 1h 4m 34s
• Includes Assessment
Likes 13
This 11-video course explores the machine learning concepts of Bayesian methods and the implementation of Bayes' theorem and methods in machine learning. Learners can examine Bayesian statistics and analysis with a focus on probability distribution and prior knowledge distribution. Begin with a look at the concept of Bayesian probability and statistical inference, then move on to the concept of Bayesian theorem and its implementation in machine learning. Next, learn about the role of probability and statistics in Bayesian analysis from the perspective of frequentist probability and subjective probability paradigms. You will examine standard probability, continuous distribution, and discrete distribution, and recall the essential elements of Bayesian statistics including prior distribution, likelihood function, and posterior inference. Recognize the implementation of prominent Bayesian methods including inference, statistical modeling, influence of prior belief, and statistical graphics. Describe prior knowledge and compare the differences between non-informative prior distribution and informative prior distribution. The steps involved in Bayesian analysis, including modeling data, deciding prior distribution, likelihood construction, and posterior distribution are also covered. The concluding exercise focuses on Bayesian statistics and analysis.

## WHAT YOU WILL LEARN

• discover the key concepts covered in this course describe the concept of Bayesian probability and statistical inference describe the concept of Bayes' theorem and its implementation in machine learning identify the role of probability and statistics in Bayesian analysis from the perspective of frequentist and subjective probability paradigms describe standard probability, continuous, and discrete distribution recall the essential ingredients of Bayesian statistics including prior distribution, likelihood function, and posterior inference
• recognize the implementation of prominent Bayesian methods including inference, statistical modeling, influence of prior belief, and statistical graphics identify the core concepts of Bayesian machine learning from the perspective of modeling, sampling algorithms, and variation inference describe prior knowledge and compare the differences between non-informative prior distribution and informative prior distribution recall the steps involved in Bayesian analysis, including modeling data, deciding prior distribution, likelihood construction, and posterior distribution specify the essential ingredients of Bayesian statistics and recall the prominent Bayesian methods and the steps involved in Bayesian analysis

## IN THIS COURSE

• 1.
Course Overview
• 2.
Bayesian Probability and Statistical Inference
• 3.
Bayes' Theorem in Machine Learning
• 4.
Frequentist and Subjective Probability
• 5.
Probability Distribution
• 6.
Ingredients of Bayesian Statistics
• 7.
Bayesian Methods
• 8.
Bayesian Concepts in ML Modeling
• 9.
Prior Knowledge Distribution
• 10.
Bayesian Analysis Approach
• 11.
Exercise: Bayesian Statistics and Analysis

## EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

Skillsoft is providing you the opportunity to earn a digital badge upon successful completion of this course, which can be shared on any social network or business platform

Digital badges are yours to keep, forever.