This is the final post in our series on random numbers in Matlab. In the first post, we discussed basic random number functions, and in the second post, we discussed the control of random number generation in Matlab and alternatives for applications with stronger requirements. In this post, we will demonstrate how to create probability distributions with the basic rand and randn functions of Matlab. This is useful in many engineering applications, including reliability analysis and communications.
This is our second post in our series on random numbers in Matlab. The first post can be found here. In this post, I will explain how to control the random number generation functions in Matlab and discuss alternatives for projects with stronger requirements for randomness, such as cryptography.
In this series of posts, I will explain how to use the various random number generation functions in Matlab. This will include the usage of the basic commands, how to control random number generation, how to create other distributions from the basic functions that Matlab provides, and what alternatives there are to the functions used in Matlab. In this post, I will explain the basic random number generation commands in Matlab, including rand, randn, randi, and randperm, and provide some example applications.
In lieu of the holiday season, Matlab Geeks is going to take a small break from writing Matlab code. Instead, we’d like to share this great article on the best practices for scientific computing. Increasingly, scientists are writing computer programs to perform their research. However, most scientists only have rudimentary training in computer programming and do not know how to create efficient, reliable, and maintainable code. This brief article lists ten recommendations that can help increase the productivity of scientists and engineers. We have summarized the main points of the paper in this article. We try to use these recommendations ourselves and believe the dissemination of this knowledge will help the scientific computing community become more productive.
A common problem in Matlab and every other programming language that uses floating point numbers is that calculations involving floats often do not yield the expected answers because of rounding, which can have undesirable effects on control statements. The immediate question is how to handle these rounding errors so that intuitively correct statements are recognized as true by a program. We would like to accomplish this while still retaining as much relevant information in the numbers as possible, thereby allowing the detection of minute differences that are not artifacts of rounding. In this post, some common errors in floating point comparisons will be discussed, as well as methods of handling these errors.