Among the aforementioned distributions, the chi-square model and Poisson distribution are discussed in detail, which can respectively help readers understand distribution approximation using chi-squared distribution and posterior histogram. The author mentioned the argument of Neyman and Scott against maximum likelihood (ML) estimation, which is a good example of connection between ML estimation and Bayesian inference. Appendix B through H respectively present derivations of distributions, form invariance, geometric prior, mean and standard deviation inference, item response theory, and fitting. [Extracted from the article]