# Tag Info

### Normalising likelihood for BIC/AIC calculation

Lower BIC scores are better, so the normalised value you're supposed to use is $\Delta BIC = BIC - BIC_{\text{min}}$, not max (see this paper, which shows the calculations for AIC, but the BIC ...
• 6,189
1 vote

### Normalising likelihood for BIC/AIC calculation

I would advise against normalizing the likelihood by the number of observations, since this would make the definitions of the BIC and the AIC irrelevant. AIC/BIC are not arbitrary combinations of a ...
• 1,644
1 vote

### How to determine if the log likelihood of logistic regression is too large or not?

The fact that you are referring to "good fit", "not good fit", and "excellent fit", is already an indication that such statements aren't objective; good, not good, and ...
• 19.7k
1 vote

### Is it practical to derive the prior distribution by dividing the posterior by the likelihood and multiplying by the "evidence"?

Mathematically, starting from given densities $\pi(\theta|x)$ and $f(x|\theta)$, there is no reason for these two functions to be compatible, namely for$$\dfrac{\pi(\theta|x)}{f(x|\theta)}$$to ...
• 94.6k
1 vote
Accepted

### What is the "direct likelihood" point of view in statistics?

Hugo, I have seen the term "Direct-Likelihood" used as a method with respect to handling missing data (aka missingness, e.g. clinical trial) via using likelihood-based mixed-effects models, ...
Accepted

### Is it practical to derive the prior distribution by dividing the posterior by the likelihood and multiplying by the "evidence"?

It is generally regarded as bad practice to decide the prior on the basis of the evidence. It would often be possible to do as you suggest: take a desired posterior distribution, divide it by the ...
• 32.4k

### Distribution check using R

Much useful information in the comments here, but I must add a point overseen by the commenters: You are trying to compare the fits by looking at their log-likelihood values. In R this is simply ...
• 67.5k
1 vote
Accepted

### Derivation of Box-Cox and Yeo-Johnson Log-Likelihood Functions

Box-Cox Transformation: Parametric family of transformations $y\mapsto y^{(\lambda) }$ defined by \begin{align}y^{(\lambda)} &:=\begin{cases}\frac{y^\lambda-1}{\lambda}~~&\lambda \ne 0\\ \ln y~...
• 1,199
1 vote
Accepted

### Coding the likelihood function for logistic regression

You need to do probability computations like this in log-space Your computation is using a naïve method that is not how we code likelihood functions (or other probability functions) in practice. When ...
• 102k
1 vote

### Where information comes for Binomial Likelihood?

I don't think your conclusion that the second factor in the likelihood (the binomial distribution) does not contain information on $N$ is true. First, we know for sure that $N\ge \max_r n_r$. Second, ...
• 67.5k
Accepted

• 101