Bayesian Methods: Bayesian Concepts & Core Components

Bayesian statistics
  • 11 Videos | 1h 4m 34s
  • Includes Assessment
  • Earns a Badge
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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

  • Playable
    1. 
    Course Overview
    1m 38s
    UP NEXT
  • Playable
    2. 
    Bayesian Probability and Statistical Inference
    7m 30s
  • Locked
    3. 
    Bayes' Theorem in Machine Learning
    5m 39s
  • Locked
    4. 
    Frequentist and Subjective Probability
    3m 35s
  • Locked
    5. 
    Probability Distribution
    6m 45s
  • Locked
    6. 
    Ingredients of Bayesian Statistics
    8m 43s
  • Locked
    7. 
    Bayesian Methods
    4m 49s
  • Locked
    8. 
    Bayesian Concepts in ML Modeling
    8m 1s
  • Locked
    9. 
    Prior Knowledge Distribution
    4m 27s
  • Locked
    10. 
    Bayesian Analysis Approach
    6m 53s
  • Locked
    11. 
    Exercise: Bayesian Statistics and Analysis
    2m 4s

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