Expectation-maximization algorithm
The Expectation-Maximization (EM) algorithm is a statistical method used to find hidden patterns in data when some information is missing or unknown. It works in two steps: the “expectation” step estimates the missing data based on the current understanding, while the “maximization” step adjusts the model parameters to improve the fit to the observed data. This process is repeated, gradually refining both the estimates and model until it converges on a solution. EM is widely used in machine learning and statistics for tasks like clustering and estimating distributions when dealing with incomplete data.
Additional Insights
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The Expectation-Maximization (EM) algorithm is a statistical method used to find hidden patterns in data with missing or incomplete information. It works in two main steps: the "Expectation" step estimates the missing values based on current information, and the "Maximization" step updates the model parameters to better fit the data. This cycle repeats until the model stabilizes, helping to maximize the likelihood of observing the data. EM is widely used in various fields, such as machine learning and data analysis, for tasks like clustering and improving predictions when data is incomplete.
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The Expectation-Maximization (EM) algorithm is a statistical method used to estimate unknown values in datasets that have missing or hidden information. It works in two steps: the "Expectation" step estimates the missing data based on current guesses, and the "Maximization" step updates those guesses to improve the overall model. By iterating between these two steps, the EM algorithm gradually refines its estimates, leading to more accurate interpretations of complex data, such as clustering similar items or inferring relationships in incomplete datasets.