Fixing Random: Techniques in C#

Gain insights into tackling uncertainty in C# programming. Delve into improving System.Random class with powerful techniques for efficient problem-solving and fewer bugs. Discover new language uses.

Advanced

44 Lessons

20h

Certificate of Completion

Gain insights into tackling uncertainty in C# programming. Delve into improving System.Random class with powerful techniques for efficient problem-solving and fewer bugs. Discover new language uses.

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

36 Playgrounds

This course includes

36 Playgrounds

Course Overview

There are a lot of problems we face in programming that deal with uncertainty, statistics, and probabilities. Unfortunately, the majority of the general-purpose languages that we use on a daily basis don’t provide a great approach to solving them. This is particularly the case in C# with the System.Random class. The implementations of this class have been pretty poor for some time. System.Random in C# regularly leads to unexpected, buggy outcomes. If you’re a C# fan who’s looking for new ways to use the l...Show More

Course Content

Introduction to System.Random in C#

Get familiar with System.Random's limitations and its impact on C# programming.

Who is This Course For?

Disadvantages of System.Random in C#

Introduction to Fixing Random

Look at improvements to C#’s randomness, probability distributions, and sampling techniques.

Fix the Trivial Problems

Introduction to Continuous Probability Distributions

Introduction to Discrete Distributions

Fixing Random - Discrete Distribution

Master the steps to implement and optimize discrete distributions in C#, including Bernoulli and alias methods.

Fixing Random - Continuous Distribution

Apply your skills to sampling and estimating expected values in continuous distributions.

Probability Distribution Function

Techniques for Sampling

Metropolis Algorithm

Posterior Distributions

Computing Continuous Posterior Distribution

Expected Value of a Discrete Distribution

What is the Average Value as the Number of Samples Increases

Better Estimation of the Expected Value

Better Estimation of the Expected Value (Continued)

Reviewing Expected Value

Correct Implementation of Importance Sampling

Attacking the Final Problem

Further Improvements

Conclusion

Explore probabilistic programming, improved tools, and efficient techniques for estimating values.

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