2019/01/20 by Benyamin Ghojogh, Aydin Ghojogh, Ghojogh, Benyamin +5 · 4 citations
Computer Science · Mathematics · #Algorithms and Data Compression #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Methodology (stat.ME) #Other Statistics (stat.OT) #Statistical Distribution Estimation and Applications #cs.LG #stat.ME #stat.ML #stat.OT
paper · pdf · doi:10.48550/arxiv.1901.06708
12 pages, 9 figures, 1 table. Some typos are corrected in this version
openalex publication_date 2019/01/20 · arxiv created 2020/10/11 · arxiv updated 2020/10/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper is a step-by-step tutorial for fitting a mixture distribution to data. It merely assumes the reader has the background of calculus and linear algebra. Other required background is briefly reviewed before explaining the main algorithm. In explaining the main algorithm, first, fitting a mixture of two distributions is detailed and examples of fitting two Gaussians and Poissons, respectively for continuous and discrete cases, are introduced. Thereafter, fitting several distributions in general case is explained and examples of several Gaussians (Gaussian Mixture Model) and Poissons are again provided. Model-based clustering, as one of the applications of mixture distributions, is also introduced. Numerical simulations are also provided for both Gaussian and Poisson examples for the sake of better clarification.