For most Bayesian model fittings, there is no analytical solution for deriving posterior distributions or integrating them. Rather, we approximate those posteriors via random sampling from all possible parameter sets and their corresponding likelihood functions. Specifically, we sample posterior distributions from a multidimensional space, where each dimension corresponds to a parameter. The shape of thisContinue reading “A simple Bayesian regression model with Stan: brms”
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Bivariate and Multivariate distributions
Univariate distributions display probability distribution for one variable, such as the number of desirable events in Binomial distribution, the variable having mean and standard deviation in Normal distribution, and the probability for each Bernoulli event in a Beta distribution. A bivariate or multivariate distribution defines probability for two or more dimensions. Assuming a PMF isContinue reading “Bivariate and Multivariate distributions”
Guest on the snow mountain 