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.

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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 language, then this course is for you. It proposes different approaches to improving the System.Random class, providing a number of powerful techniques that solve problems more efficiently and with fewer lines of code.

Fixing Random: Techniques in C#

In the [previous lesson](https://www.educative.io/courses/fixing-random-techniques-in-c-sharp/techniques-for-sampling), we implemented a technique for sampling from a non-normalized target PDF:

* Find an everywhere-larger helper PDF that we can sample from.
* Sample from it.
* Accept or reject the sample via a coin flip with the ratio of weights in the target distribution and the helper distribution.

This technique works, but it has a few drawbacks:

* It’s not at all clear how to find a suitable helper PDF without humans intervening.
* Even with a pretty tight-fitting helper, you potentially still end up rejecting a lot of samples.

The first problem is the big one. It would be great if there were a technique that didn’t require quite so much intervention from experts.

In this lesson, we’ll describe just such a technique; it is called the "**Metropolis algorithm**" after one of its inventors, Nicholas Metropolis.

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## Introduction to the Metropolis Algorithm

In the [previous lesson](https://www.educative.io/courses/fixing-random-techniques-in-c-sharp/techniques-for-sampling), we implemented a technique for sampling from a non-normalized target PDF:

* Find an everywhere-larger helper PDF that we can sample from.
* Sample from it.
* Accept or reject the sample via a coin flip with the ratio of weights in the target distribution and the helper distribution.

This technique works, but it has a few drawbacks:

* It’s not at all clear how to find a suitable helper PDF without humans intervening.
* Even with a pretty tight-fitting helper, you potentially still end up rejecting a lot of samples.

The first problem is the big one. It would be great if there were a technique that didn’t require quite so much intervention from experts.

In this lesson, we’ll describe just such a technique; it is called the "**Metropolis algorithm**" after one of its inventors, Nicholas Metropolis.

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# Introduction to the Metropolis Algorithm

In this lesson, we will learn about the Metropolis Algorithm, and its implementation.

Metropolis Algorithm

Introduction to System.Random in C#

Introduction to Fixing Random

Fixing Random - Discrete Distribution

Fixing Random - Continuous Distribution

Conclusion

Appendix

Metropolis Algorithm

Introduction to the Metropolis Algorithm