# Tag Info

### Choosing between uninformative beta priors

First of all, there is no such a thing as uninformative prior. Below you can see posterior distributions resulting from five different "uninformative" priors (described below the plot) given different ...
Accepted

### Is the Gaussian distribution a specific case of the Beta Distribution?

They are both symmetric and more or less bell shaped, but the symmetric beta (whether at 4,4 or at any other specific value) is not actually Gaussian. You can tell this even without looking at the ...

### How to implement a mixed model using betareg function in R?

The package glmmTMB may be helpful for anyone with a similar question. For example, if you wanted to include pond from the above question as a random effect, the following code would do the trick: <...
Accepted

### Beta distribution on flipping a coin

The quotation is a "logical sleight-of-hand" (great expression!), as noted by @whuber in comments to the OP. The only thing we can really say after seeing that the coin has an head and a tail, is that ...
Accepted

### Why exactly can't beta regression deal with 0s and 1s in the response variable?

Because the loglikelihood contains both $\log(x)$ and $\log(1-x)$, which are unbounded when $x=0$ or $x=1$. See equation (4) of Smithson & Verkuilen, "A Better Lemon Squeezer? Maximum-Likelihood ...

### Whence the beta distribution?

Thomas Bayes (1763) derived the Beta distribution [without using this name] as the very first example of posterior distribution, predating Leonhard Euler (1766) work on the Beta integral pointed out ...
Accepted

### What is the relationship between the Beta distribution and the logistic regression model?

Beta is a distribution of values in $(0,1)$ range that is very flexible in it's shape, so for almost any unimodal empirical distribution of values in $(0,1)$ you can easily find parameters of such ...
Accepted

### Efficiently sampling a thresholded Beta distribution

The simplest way, and most general way, that applies to any truncated distribution (it can be also generalized to truncation on both sides), is to use inverse transform sampling. If $F$ is the ...
Accepted