Gradient ascent is an optimization algorithm used to find the maximum of a function by iteratively moving in the direction of the steepest increase in the function's value. This technique is particularly relevant in Maximum a posteriori (MAP) estimation, where it helps in maximizing the posterior distribution by adjusting parameters in a way that enhances the likelihood of observing the given data, thereby leading to better estimates.
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