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I'm fairly knowledgeable about Bayesian statistics, but I can't tell which technique you're referring to. It vaguely sounds like maximum likelihood, but ML does
by ced 13y ago
I'm fairly knowledgeable about Bayesian statistics, but I can't tell which technique you're referring to. It vaguely sounds like maximum likelihood, but ML doesn't iterate the model or priors. Could you explain further and give a reference?
- tprynn 13y agoIterative algorithms which attempt to solve maximum likelihood usually fall under the category of expectation maximization (a good example is K-means clustering). http://en.wikipedia.org/wiki/Expectation-maximization_algorithm http://en.wikipedia.org/wiki/Expectation-maximization_algori...
- ced 13y agoAh, I'd always thought of EM as iterating over the parameters given a model, but I suppose it makes sense to see it as a search for a model, including parameters, that fits the data. Cool.
- pavanred 13y agoPerhaps he/she is referring to conjugate priors [1], where the prior and the posterior are of the same family of distributions. Once you compute the posterior using a likelihood and prior, you obtain a posterior that is of the same family of distribution as the prior. This posterior can then be used as a prior for more samples. [1] http://en.wikipedia.org/wiki/Conjugate_prior http://en.wikipedia.org/wiki/Conjugate_prior