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Maximum A Posteriori (MAP) - 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 | ||||||||
================================================================================= MAP can refer to the Maximum A Posteriori estimation, which is a statistical method used to estimate the most likely value of a parameter or set of parameters in a probabilistic model. It is often used in Bayesian statistics and is related to maximum likelihood estimation. That is, MAP estimation is a point estimation method that seeks to find the most probable value of a parameter θ given the data s. It is represented as θ_MAP and is calculated as: θMAP = argmax(p(θ|s)) --------------------------------------------- [3800] In other words, it finds the value of θ that maximizes the posterior probability p(θ|s). The MAP estimate incorporates both the likelihood of the data given the parameter (p(s|θ)) and the prior belief about the parameter (p(θ)). ============================================
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