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Fixing Random: Techniques in C#

Fixing Random: Techniques in C#

Advanced

44 Lessons

20h

Certificate of Completion

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This course includes

36 Playgrounds

This course includes

36 Playgrounds
Course Overview
Course Content

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

1.

Introduction to System.Random in C#

Who is This Course For?
Disadvantages of System.Random in C#
2.

Introduction to Fixing Random

Fix the Trivial Problems
Introduction to Continuous Probability Distributions
Introduction to Discrete Distributions
3.

Fixing Random - Discrete Distribution

Singleton Distribution
Discrete Distribution on C# Objects
Non-uniform Discrete Distribution
Non-uniform Discrete Distribution (Continued)
Alias Method
Conditional Probabilistic Reasoning
Efficiently Sampling a Conditioned Distribution
Likelihood Functions
Probability Distribution Monad
Probability Distribution Monad (Continued)
Probability Distribution Monad with Round Numbers
Solve Problems in Bayesian Inference
Discrete Probability Distribution Type as a Monad
Monty Hall Problem
Empty Distribution
Musings about Language Design
Proposed Stripped-Down C#
Converting to Ordinary C# 7
Challenges for Improving Imperative Probabilistic Workflows
Problems with Discrete Probability Distributions
Model Markov Processes
An Application of Markov Model
4.

Fixing Random - Continuous Distribution

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
5.

Conclusion

More on Fixing Random
A Final Word
6.

Appendix

2 Lessons

Course Author

Trusted by 1.4 million developers working at companies

Anthony Walker

@_webarchitect_

Evan Dunbar

ML Engineer

Carlos Matias La Borde

Software Developer

Souvik Kundu

Front-end Developer

Vinay Krishnaiah

Software Developer

Eric Downs

Musician/Entrepeneur

Kenan Eyvazov

DevOps Engineer

Souvik Kundu

Front-end Developer

Eric Downs

Musician/Entrepeneur

Anthony Walker

@_webarchitect_

Evan Dunbar

ML Engineer

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