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Maximum Likelihood Estimation

Maximum Likelihood Estimation (MLE) is a statistical method used to find the best-fitting model for a set of data. It works by identifying the parameters of a model that make the observed data most probable. Essentially, MLE estimates what values for these parameters would lead to the highest likelihood of the data we have seen. For example, if we wanted to estimate the average height of a group of people based on a sample, MLE would suggest the average that makes the observed heights most likely. It’s widely used in statistics, machine learning, and various fields of research.

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    Maximum likelihood estimation (MLE) is a statistical method used to estimate the parameters of a model. It works by finding the values of the parameters that make the observed data most probable. Imagine you have a bag of different colored marbles, and you want to guess the proportion of each color. MLE helps you estimate these proportions by calculating which values would most likely produce the results you see. Essentially, it helps you find the best-fit parameters that maximize the likelihood of your data, providing a way to understand and predict based on what you've observed.