Johns Hopkins University
Advanced Probability and Statistical Methods

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Johns Hopkins University

Advanced Probability and Statistical Methods

Ian McCulloh
Tony Johnson

Instructors: Ian McCulloh

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

47 hours to complete
3 weeks at 15 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

47 hours to complete
3 weeks at 15 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Learn to analyze relationships between random variables through joint probability distributions and independence concepts.

  • Understand how to calculate and interpret expected values, variances, and correlations for random variables.

  • Acquire essential skills in conducting statistical tests, including T-tests and confidence intervals, for data analysis.

  • Explore the principles of Markov chains and their applications in modeling systems with memoryless properties and calculating entropy.

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Recently updated!

October 2024

Assessments

22 assignments

Taught in English

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This course is part of the Statistical Methods for Computer Science Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 6 modules in this course

This course provides a comprehensive overview of probability theory and statistical inference, covering joint probability distributions, independence, and conditional distributions. Students will explore expected values, variances, and key statistical theorems, including the central limit theorem. Hypothesis testing, regression analysis, and stochastic processes such as Poisson processes and Markov chains will also be examined. Through practical applications and problem-solving, participants will gain essential skills in data analysis and interpretation.

What's included

2 readings1 plugin

This module presents the joint distributions of multiple random variables, both discrete and continuous and introduces the concept of independence.

What's included

9 videos4 readings5 assignments1 ungraded lab

This module focuses on the expectation of a random variable and joint random variable. Students will solve problems using the linearity of expectation and identify when its application is inappropriate. We will also explore variance, covariance, and correlation.

What's included

7 videos3 readings4 assignments1 ungraded lab

This module will apply several limit theorems to solve problems to include the central limit theorem, the Markov inequality, and the Chebyshev inequality. We will also prove Murphy’s Law.

What's included

9 videos4 readings5 assignments1 ungraded lab

This module develops student proficiency in probabilistic models to include Markov chains. Students will be introduced to problems involving surprise, uncertainty, and entropy.

What's included

4 videos2 readings3 assignments1 ungraded lab

This module develops student proficiency in probabilistic models to include Markov chains. Students will be introduced to problems involving surprise, uncertainty, and entropy.

What's included

8 videos4 readings5 assignments1 ungraded lab

Instructors

Ian McCulloh
Johns Hopkins University
10 Courses399 learners
Tony Johnson
Johns Hopkins University
3 Courses92 learners

Offered by

Recommended if you're interested in Probability and Statistics

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