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Approximate Inference - Python Automation and Machine Learning for ICs - - An Online Book - |
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Python Automation and Machine Learning for ICs 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 | ||||||||
================================================================================= Approximate inference in machine learning refers to the process of estimating complex probabilistic models when exact inference is computationally intractable. Many machine learning models involve dealing with probability distributions over a large number of variables, and computing the exact posterior distribution can be challenging or impossible due to the complexity of the model. In cases where exact inference is too computationally expensive or impractical, approximate inference methods are used to provide a close approximation of the true posterior distribution. These methods trade accuracy for computational efficiency, allowing practitioners to make reasonable approximations and gain insights into the model's behavior. Some common techniques for approximate inference include:
These methods allow practitioners to make probabilistic inferences even when exact solutions are challenging. The choice of the approximate inference method depends on the specific characteristics of the model and the computational resources available.
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