Bayesian Methods: Bayesian Concepts & Core Components
Bayesian statistics
| Intermediate
- 11 Videos | 1h 4m 34s
- Includes Assessment
- Earns a Badge
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
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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
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1.Course Overview1m 38sUP NEXT
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2.Bayesian Probability and Statistical Inference7m 30s
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3.Bayes' Theorem in Machine Learning5m 39s
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4.Frequentist and Subjective Probability3m 35s
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5.Probability Distribution6m 45s
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6.Ingredients of Bayesian Statistics8m 43s
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7.Bayesian Methods4m 49s
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8.Bayesian Concepts in ML Modeling8m 1s
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9.Prior Knowledge Distribution4m 27s
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10.Bayesian Analysis Approach6m 53s
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11.Exercise: Bayesian Statistics and Analysis2m 4s
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.
