Probabilistic Model
A probabilistic model is a mathematical framework for representing uncertain knowledge or incomplete information about a system using probability theory. In data science, probabilistic models are used to make predictions or decisions based on data that contains uncertainty or noise. These models can be used for a variety of tasks, such as classification, regression, clustering, and anomaly detection. Probabilistic models can be parametric or non-parametric, depending on whether they have a fixed number of parameters or not. Examples of probabilistic models include Bayesian networks, Markov models, Gaussian mixture models, and Hidden Markov models.
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