Probabilistic Model/Algorithm  Python for Integrated Circuits   An Online Book  

Python for Integrated Circuits http://www.globalsino.com/ICs/  


Chapter/Index: Introduction  A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z  Appendix  
================================================================================= In machine learning, a probabilistic model is a type of model that incorporates uncertainty into its predictions. Unlike deterministic models that produce a single output for a given input, probabilistic models provide a probability distribution over possible outcomes. These models are particularly useful when dealing with uncertain or noisy data, as they can capture the inherent uncertainty in the data and provide richer and more informative predictions. There are several types of probabilistic models in machine learning, including:
Probabilistic models are essential in various machine learning tasks, such as classification, regression, clustering, and generative modeling, where accounting for uncertainty can lead to more robust and informative predictions. They are also crucial in Bayesian inference, which is used for parameter estimation, model selection, and uncertainty quantification in many applications. ============================================ Table 3966. Application examples of probabilistic model.


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