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- What is the difference between likelihood and probability?
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 usage there is a
- What is the conceptual difference between posterior and likelihood . . .
2 To put simply, likelihood is "the likelihood of $\theta$ having generated $\mathcal {D}$ " and posterior is essentially "the likelihood of $\theta$ having generated $\mathcal {D}$ " further multiplied by the prior distribution of $\theta$ If the prior distribution is flat (or non-informative), likelihood is exactly the same as posterior
- Confusion about concept of likelihood vs. probability
Likelihood is simply an "inverse" concept with respect to conditional probability However, there seems to be something of a disingenuous sleight of hand here: on a purely colloquial level, likelihood, i e how likely something is, is about as far away from an inverse concept of probability (i e how probable something is), as can be
- Theoretical motivation for using log-likelihood vs likelihood
I'm trying to understand at a deeper level the ubiquity of log-likelihood (and perhaps more generally log-probability) in statistics and probability theory Log-probabilities show up all over the
- estimation - Likelihood vs quasi-likelihood vs pseudo-likelihood and . . .
The concept of likelihood can help estimate the value of the mean and standard deviation that would most likely produce these observations We can also use this for estimating the beta coefficient of a regression model I am having a bit of difficulty understanding the quasi likelihood and the restricted likelihood
- What is the difference between priors and likelihood?
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 you elaborate?
- Optimizing Gaussian negative log-likelihood - Cross Validated
The regular Gaussian likelihood of a single value , given parameters and would be: I used instead of to avoid confusion later In order to optimize a neural network one needs it's logarithm You can use property of the logarithm: and separate the normalizing "constant": For the second term you can just drop the logarithm, because : In most cases the first term is an additive constant so it
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