Gradient ascent is an optimization algorithm used to maximize a function by iteratively adjusting the parameters in the direction of the steepest increase of the function's value. This technique is crucial in statistical estimation methods, particularly in maximizing likelihood functions to determine the best parameters that explain the observed data. It connects closely with methods like maximum likelihood estimation, as it provides a systematic way to identify parameter values that enhance the fit of a model to data.
congrats on reading the definition of gradient ascent. now let's actually learn it.