IPython Interactive Computing and Visualization Cookbook by Cyrille Rossant

Author:Cyrille Rossant [Rossant, Cyrille]
Language: eng
Format: epub, pdf
Publisher: Packt Publishing
Published: 2014-09-24T21:00:00+00:00

This equation gives us information about our model, knowing the observed data. Bayes' equation is widely used in signal processing, statistics, machine learning, inverse problems, and in many other scientific applications.

In Bayes' equation, P(model) reflects our prior knowledge about the model. Also, P(data) is the distribution of the data. It is generally expressed as an integral of P(data|model)P(model).

In conclusion, Bayes' equation gives us a general roadmap for data inference:

Specify a mathematical model for the direct process model → data (the P(data|model) term).

Specify a prior probability distribution for the model (P(model) term).

Perform analytical or numerical calculations to solve this equation.

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