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The probabilistic system is based on the theory of probability which involves chance variation. Application of probability should be learned from this course to do modeling, qualification, and analysis of uncertainty.

You are provided in this [course_title] the basics and tool of probability theory. It will provide you information of the related field of statistical inference, which is the key to analysis, and make sense of the data available.

Assessment

This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.

Certification

Edukite courses are free to study. To successfully complete a course you must submit all the assignment of the course as part of the assessment. Upon successful completion of a course, you can choose to make your achievement formal by obtaining your Certificate at a cost of £49.

Having an Official Edukite Certification is a great way to celebrate and share your success. You can:

• Show it to prove your success

Course Credit: MIT

Course Curriculum

 Module: 1 Lecture 1: Probability Models and Axioms Video 00:51:00 Lecture 2: Conditioning and Bayes’ Rule Video 00:51:00 Lecture 3: Independence Video 00:46:00 Lecture 4: Counting Video 00:52:00 Lecture 5: Discrete Random Variables; Probability Mass Functions; Expectations Video 00:51:00 Lecture 6: Discrete Random Variable Examples; Joint PMFs Video 00:51:00 Module: 2 Lecture 7: Multiple Discrete Random Variables Video 00:51:00 Lecture 8: Continuous Random Variables Video 00:50:00 Lecture 9: Multiple Continuous Random Variables Video 00:51:00 Lecture 10: Continuous Bayes’ Rule; Derived Distributions Video 00:49:00 Lecture 11: Derived Distributions; Convolution; Covariance and Correlation Video 00:52:00 Lecture 12: Iterated Expectations; Sum of a Random Number of Random Variables Video 00:48:00 Module: 3 Lecture 13: Bernoulli Process Video 00:51:00 Lecture 14: Poisson Process – I Video 00:53:00 Lecture 15: Poisson Process – II Video 00:49:00 Lecture 16: Markov Chains – I Video 00:52:00 Lecture 17: Markov Chains – II Video 00:51:00 Lecture 18: Markov Chains – III Video 00:52:00 Lecture 19: Weak Law of Large Numbers Video 00:50:00 Module: 4 Lecture 20: Central Limit Theorem Video 00:51:00 Lecture 21: Bayesian Statistical Inference – I Video 00:49:00 Lecture 22: Bayesian Statistical Inference – II Video 00:52:00 Lecture 23 Classical Statistical Inference – I Video 00:50:00 Lecture 24: Classical Inference – II Video 00:51:00 Lecture 25: Classical Inference – III Video 00:52:00 Assessment Submit Your Assignment 00:00:00 Certification 00:00:00

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