Fixing Random: Techniques in C#
INTERACTIVE COURSE

Fixing Random: Techniques in C#

Advanced

44 Lessons

20h

Certificate of Completion

Fixing Random: Techniques in C#

This course includes:

36 Playgrounds
35 Illustrations

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 ProblemsIntroduction to Continuous Probability Distributions
Introduction to Discrete Distributions
3

Fixing Random - Discrete Distribution

Singleton Distribution
Discrete Distribution on C# ObjectsNon-uniform Discrete DistributionNon-uniform Discrete Distribution (Continued)Alias MethodConditional Probabilistic ReasoningEfficiently Sampling a Conditioned DistributionLikelihood FunctionsProbability Distribution MonadProbability Distribution Monad (Continued)Probability Distribution Monad with Round NumbersSolve Problems in Bayesian InferenceDiscrete Probability Distribution Type as a MonadMonty Hall ProblemEmpty DistributionMusings about Language DesignProposed Stripped-Down C#Converting to Ordinary C# 7Challenges for Improving Imperative Probabilistic WorkflowsProblems with Discrete Probability DistributionsModel Markov ProcessesAn Application of Markov Model
4

Fixing Random - Continuous Distribution

Probability Distribution Function
Techniques for SamplingMetropolis AlgorithmPosterior DistributionsComputing Continuous Posterior DistributionExpected Value of a Discrete DistributionWhat is the Average Value as the Number of Samples IncreasesBetter Estimation of the Expected ValueBetter Estimation of the Expected Value (Continued)Reviewing Expected ValueCorrect Implementation of Importance SamplingAttacking the Final ProblemFurther Improvements
5

Conclusion

More on Fixing RandomA Final Word
6

Appendix

2 Lessons

COURSE AUTHOR
Eric Lippert

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