# Tag Info

## New answers tagged likelihood

1 vote

### Why my Rho-square on Multinomial Logit Model (McFadden) so small?

$R^2$ measures do not assess goodness of fit. They quantify predictiveness of outcomes. $R^{2}=0$ may be an excellent fit, i.e., every observation may have the same probability of an outcome. Some ...
• 95.7k

### Weighted likelihood in Bayes' rule

Here's my very personal take on it. I have seen this proposal of weighting the likelihood several times over the years. It's a fairly natural way to extend Bayesian inference, and it comes up from ...
• 2,471

### Weighted likelihood in Bayes' rule

While this is beyond statistics, exponential weights can be added to both the likelihood and the prior to introduce (or fix) certain types of biases. $$P(H|D) \propto P(D|H)^\beta P(H)^\alpha$$ ...
• 1,440
Accepted

### Basic question about deriving MAP estimator

If $t$ is a random parameter, for which Bayesian inference is required, then $t$ should have a prior, $p(t)$, so it can have a posterior and a MAP estimate. In the most general case, \$t_{MAP}=\arg\...
• 2,258

### Confusion over Fisher-scoring algorithm

TLDR: always use line-search gradient descent or the BFGS algorithm to find the MLE. Fisher scoring is a bad idea. To discuss this method, we need to compare it to other methods to find the MLE. Let's ...
• 2,471
Accepted

### Confusion over Fisher-scoring algorithm

Yes, but that matters less than you might think For canonical-link generalised linear models, which are a very popular special case, the algorithm is exactly Newton-Raphson For regression models more ...
• 41.4k
1 vote

### Negative log-likelihood, high BIC, high R-squared, low error, using a difference-in-differences (DiD) methodology

Beyond @PeterFlom's excellent answer about the relative nature of metrics like log-likelihood and BIC, you're missing a critical point: never use a Poisson model without considering the possibility of ...
• 44.5k

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