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In Bayesian statistics a prior distribution formalizes information or knowledge (often subjective), available before a sample is seen, in the form of a probability distribution. A distribution with large spread is used when little is known about the parameter(s), while a more narrow prior distribution represents a greater degree of information.

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If $\alpha$ is the intercept, then I think it should be specified on the log pounds scale. The reason is pretty clear. Assume all the other covariates are mean centered, and then consider the linear …
answered 20 hours ago by Demetri Pananos
1
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1answer
In the middle of page 64 of the third edition of Bayesian Data Analysis, Gelman writes... We saw in Chapter 2 that a sensibly vague prior for $\mu$ and $\sigma^2$, assuming prior indipendance of … scale and location, is uniform on $(\mu, \sigma^2)$, or eqivalently $$ p(\mu, \sigma^2) \propto (\sigma^2)^{-1} $$ I don't think I saw where this was referenced. Gelman writes that a prior or a …
asked Jun 6 '18 by Demetri Pananos
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bernoulli experiments num_trials = 1 #Num experiments #Flat prior prior = dbeta(theta,1,1) #Note here that true_theta is the prob argument. #Results in a vector of length num_trials, each with N … of each function so they are plotted on a reasonable scale plot(theta,prior/max(prior), type = 'l') lines(theta,likelihood/max(likelihood), col='blue') lines(theta,posterior/max(posterior), col = 'red …
answered Apr 3 by Demetri Pananos