Note Set 3: Models, Parameters, and Likelihood
نویسنده
چکیده
1. Construct a generative or forward model M with parameters θ of how data D can be generated. We can think of this generative model as a stochastic simulator for the data, with parameters θ. We will assume for now that M , the structure or functional form of the model, is known, but that the parameters θ are unknown1. An example would be that M is a Gaussian (Normal) probability density function with unknown parameters θ = {μ, σ2}.
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