The likelihood is the joint density of the data, given a parameter value and the prior is the marginal distribution of the parameter. Something tells me you're asking something more though-- can …

Explore related questions distributions self-study exponential-distribution likelihood-ratio f-test See similar questions with these tags.

That's not exactly what Casella and Berger say. They recognize (page 319) that when the transformation is one-to-one the proof of the invariance property is very simple. But then they …

It follows that if you minimize the negative log-likelihood, the returned Hessian is the equivalent of the observed Fisher information matrix whereas in the case that you maximize the log …

We want to construct a likelihood ratio test to see if a Poisson distribution is suitable to describe the data. The likelihood and log-likelihood equations for a Poisson distribution are:

Oct 3, 2019 · 2 To put simply, likelihood is "the likelihood of $\theta$ having generated $\mathcal {D}$ " and posterior is essentially "the likelihood of $\theta$ having generated $\mathcal {D}$ " …

I agree that the likelihood doesn't need to be normalized, but for a particular data set the likelihood function is considered a measurable mapping from parameter space to R. You can use that …

Mar 5, 2012 · The wikipedia page claims that likelihood and probability are distinct concepts. In non-technical parlance, "likelihood" is usually a synonym for "probability," but in statistical …

I think the confusion may be due to thinking of the log-likelihood when you are really looking at the log of the likelihood ratio (likelihood for model1/likelihood for model2). So depending on which …

Jan 10, 2015 · My question is, how do I determine the likelihood function? I looked up the pdf of the exponential distribution, but it's different. So is the likelihood function always given to me in …