Which approach chooses parameters that maximize the probability of observing the data given a model?

Get ready for the GARP Risk and AI Exam with flashcards and multiple choice questions. Each question comes with hints and explanations. Prepare for success!

Multiple Choice

Which approach chooses parameters that maximize the probability of observing the data given a model?

The idea being tested is choosing parameters to make the observed data as probable as possible under the model. Maximum Likelihood Estimation does exactly that: it defines a likelihood function L(θ) = P(D | θ, M) for the data D given model M and parameters θ, and selects the θ that maximize this likelihood. In practice we often maximize the log-likelihood because sums are easier to work with and numerically stable. The result is parameter values that make the observed data most plausible under the assumed model family. The other approaches are optimization methods or techniques for different goals. Gradient descent methods are general ways to minimize a loss function; they can be used to maximize likelihood if you frame the objective as maximizing the log-likelihood (equivalently minimizing negative log-likelihood), but they’re not by themselves the method that defines the goal of fitting data. The policy gradient approach comes from reinforcement learning and aims to maximize expected rewards, not the probability of the observed data under a statistical model.

The idea being tested is choosing parameters to make the observed data as probable as possible under the model. Maximum Likelihood Estimation does exactly that: it defines a likelihood function L(θ) = P(D | θ, M) for the data D given model M and parameters θ, and selects the θ that maximize this likelihood. In practice we often maximize the log-likelihood because sums are easier to work with and numerically stable. The result is parameter values that make the observed data most plausible under the assumed model family.

The other approaches are optimization methods or techniques for different goals. Gradient descent methods are general ways to minimize a loss function; they can be used to maximize likelihood if you frame the objective as maximizing the log-likelihood (equivalently minimizing negative log-likelihood), but they’re not by themselves the method that defines the goal of fitting data. The policy gradient approach comes from reinforcement learning and aims to maximize expected rewards, not the probability of the observed data under a statistical model.

More practice questions