# Bayesian Methods: Bayesian Concepts & Core Components

Bayesian statistics    |    Intermediate
• 11 videos | 1h 4s
• Includes Assessment
Likes 18
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

• Upon completion of this video, you will be able to describe the concept of Bayesian probability and statistical inference.
• 3.  Bayes' Theorem in Machine Learning
After completing this video, you will be able to describe the concept of Bayes' theorem and how it is implemented in machine learning.
• 4.  Frequentist and Subjective Probability
In this video, you will identify the role of probability and statistics in Bayesian analysis from the perspective of the frequentist and subjective probability paradigms.
• 5.  Probability Distribution
After completing this video, you will be able to describe standard probability, continuous, and discrete distributions.
• 6.  Ingredients of Bayesian Statistics
Upon completion of this video, you will be able to recall the essential ingredients of Bayesian statistics, including the prior distribution, likelihood function, and posterior inference.
• 7.  Bayesian Methods
Upon completion of this video, you will be able to recognize the implementation of prominent Bayesian methods, including inference, statistical modeling, influence of prior belief, and statistical graphics.
• 8.  Bayesian Concepts in ML Modeling
During this video, you will learn how to identify the core concepts of Bayesian machine learning from the perspective of modeling, sampling algorithms, and variation inference.
• 9.  Prior Knowledge Distribution
Upon completion of this video, you will be able to describe prior knowledge and compare the differences between non-informative and informative prior distributions.
• 10.  Bayesian Analysis Approach
Upon completion of this video, you will be able to recall the steps involved in Bayesian analysis, including modeling data, deciding on a prior distribution, constructing a likelihood, and deriving a posterior distribution.
• 11.  Exercise: Bayesian Statistics and Analysis
After completing this video, you will be able to specify the essential ingredients of Bayesian statistics and recall the prominent Bayesian methods and the steps involved in Bayesian analysis.

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