MATLAB Probability and Statistical Computing

Application Background: With modern systems becoming increasingly complex, predicting system fault probabilities has become a prominent topic. To assist beginners in learning and practice, we provide introductions to 30 MATLAB probability and statistical prediction methods along with their source code examples.

Key Technology: When programming probability and statistical predictions in MATLAB, it's crucial to first understand prediction principles and establish mathematical models. For beginners, we recommend starting with classical methods like linear regression analysis and multiple nonlinear regression. The implementation typically involves using MATLAB's built-in functions such as regress() for linear regression or nlinfit() for nonlinear curve fitting. Understanding statistical calculation methods for various fault prediction approaches, including probability density functions and confidence interval calculations using functions like normpdf() and norminv(), will contribute to more accurate system fault probability predictions. Beginners should practice modifying existing code templates to gradually master advanced techniques.