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Posterior Distribution - 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 | ||||||||
================================================================================= Posterior distribution is the distribution of the model parameters after taking into account both the prior information (parameter distribution) and the observed data. It is calculated using Bayes' theorem, which combines the prior distribution and the likelihood function to update our beliefs about the parameters based on the observed data. The posterior distribution provides a complete representation of uncertainty about the model parameters given the available data. ============================================
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