Probability made easy: Continuous randomness

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  • Lectures 37
  • Length 2 hours
  • Skill Level Intermediate Level
  • Languages English
  • Includes Lifetime access
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    Available on iOS and Android
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About This Course

Published 1/2016 English

Course Description

This course is a part in a series of courses that covers a complete course in probability theory taught in the US colleges.

In this course you will learn continuous probability theory. You will learn how to predict the results of continuous random events and how to predict how much on average the actual results would deviate from your estimate.

If you are looking for an efficient way to learn/review probability theory, this course is for you. With dynamic slides, each hour of the series covers the material of more than a week of regular live lectures!

You may review lectures as many times as you want, skip easy exercises when the material is familiar, choose your own pace! If you need help, I will answer every question you post on the discussion board. If you are not completely satisfied, I offer a 30 day money back guarantee.

What are the requirements?

  • familiarity with such notions as outcomes, events and probability

What am I going to get from this course?

  • learn how to predict the results of continuous random events (expected value)
  • learn how to predict how much on average the actual results would deviate from your estimate (standard deviation)
  • 18 theoretic video lectures and lectures with motivations
  • 17 practice video lectures marked as easy, intermediate and advanced
  • more than 50 pages of transcripts of video lectures
  • hundreds of slides, and
  • my help (post your questions on the board!)

What is the target audience?

  • students who would like an efficient tutorial
  • students who would like a review before an exam

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

Section 1: Introduction
Introduction
Preview
01:40
Section 2: Continuous Random Variable
Motivation. Problem solving (easy)
Preview
04:33
Motivation. Problem solving (intermediate)
Preview
02:42
Continuous random variable
Preview
01:28
Section 3: Distribution and Density Functions
Probability distribution function: Motivation
01:56
Probability density function: Definition
02:27
Problem solving (easy)
03:55
Problem solving (intermediate)
04:04
Cumulative distribution function
04:09
Section 4: Uniform Random Variable
Uniform random variable
03:34
Problems solving (easy)
01:34
Problem solving (intermediate)
05:12
Section 5: Conditional Probability
Conditional probability: definition
03:21
Conditional Probability: theorem
03:18
Problem solving (easy)
02:43
Problem solving (easy)
02:18
Section 6: Exponential Density
Exponential density
04:35
Problem solving (easy)
07:47
Problem solving (easy)
03:41
Memorylessness
01:52
Problem solving (advanced)
02:52
Section 7: Independence of Random Variables
Two properties of distribution functions
01:28
Independent random variables
04:00
Joint distribution functions
02:34
Problem solving (easy)
01:31
Joint density function
02:59
Problem solving (easy)
01:19
Independence of random variables: Theorem
03:55
Density functions computed from the joint density function (trick)
05:05
Problem solving (intermediate)
04:58
Problem solving (intermediate)
02:17
Section 8: Expected Value and Variance
Expected value
01:43
Properties of expected value
01:45
Problem solving (easy)
03:53
Variance
02:16
Problem solving (easy)
03:49
Section 9: Thank you!
Thank you!
1 page

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Instructor Biography

I am a professional mathematician. As a mathematician I worked in US, Mexico, Japan, Canada and Germany. I am an author of 16 research papers published in internationally renowned journals.

I taught courses ranging from middle school to PhD level, including such high level courses as Topological Quantum Field Theory, Moduli spaces of Riemann curves, and Topological Surgery Theory.

What you may expect from me is a well structured course with clearly explained notions and theory.


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