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:

- Add the certificate to your CV or resume and brighten up your career
- 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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