Maximum Likelihood Cost Function for Conditional Distributions
The maximum likelihood cost function for learning conditional distributions is defined as the expected negative log-likelihood: . The specific form of this cost function changes depending on the choice of model, specifically the form of . An advantage of this approach is that it removes the burden of manually designing cost functions for each specific model, as the objective is automatically determined once the conditional probability distribution is specified.
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A researcher is modeling a series of coin flips. They observe the following sequence of outcomes: Heads, Tails, Heads, Heads. The researcher wants to find the best parameter for their model, where the parameter represents the probability of the coin landing on Heads. According to the principle of maximum likelihood estimation, which of the following parameter values best explains the observed data?
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