Maximum likelihood
نویسندگان
چکیده
Assume that we have some data D and a model M of the process that generated the data. The model has some parameters θ, the specific value of which we do not know but wish to estimate. If the model is properly constructed, we will be able to calculate the probability of it generating the observed data given a specific set of parameter values, P (D|θ,M). Often, the conditioning on the model is suppressed in the notation, in which case the probability would simply be written as P (D|θ). This probability is often referred to as the likelihood of the parameter values. In maximum likelihood inference, we simply estimate the unknowns in θ by finding the values with the maximum likelihood or, more precisely, the highest probability of generating the observed data.
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