In this segment, you will study the basic idea of generative models in the context of topic modelling.
Informally speaking, in generative probabilistic modelling, we assume that the data (which we observe , such as a set of document) is ‘generated’ through a probabilistic model. then , using the observed data points, we try to infer the parameters of the probability of observing the data.
For example, you can see POS tagged words as data generated from a probabilistic model such as an HMM. you then try to infer the HMM model parameter which maximise the probability of the observed data.
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