Fitting Various Common Distributions

To fit various common distribution programs such as the Weibull distribution, Rayleigh distribution, K-distribution, and others, we utilize the MATLAB programming language. This implementation enables more accurate data analysis and modeling by leveraging distribution-specific properties through key MATLAB functions including wblfit for Weibull distribution fitting, raylfit for Rayleigh distribution estimation, and custom maximum likelihood estimation algorithms for K-distribution parameter calculation. The framework employs statistical fitting techniques including maximum likelihood estimation (MLE) and probability plot correlation coefficient (PPCC) methods to optimize distribution parameters. By harnessing MATLAB's computational capabilities, we gain deeper insights into underlying data patterns and trends, facilitating informed decision-making and improved outcomes. MATLAB's flexibility allows for straightforward model customization and fine-tuning through functions like mle for custom distribution fitting and dfittool for interactive distribution analysis, ensuring updated and accurate information processing. Overall, this MATLAB-based distribution modeling approach represents significant advancement in complex dataset analysis, with ongoing exploration of additional statistical toolboxes and machine learning integration for enhanced analytical innovation.