[basic knowledge of deep learning - 46] Bayesian theorem and conditional probability formula

Fundamental theorem

• Bayes is based on Bayes theorem in probability theory , Bayesian theorem is to use a priori probability and conditional probability to calculate the final event probability .
• Bayesian network can be understood as taking the model as a probability density function , It can represent the distribution of data , The training process is the parameter estimation process of probability distribution , The prediction process is the process of solving the conditional probability .

The posterior probability is obtained by conditional probability

• A posteriori probability can be expressed by conditional probability , Formula for ：
  
  It can be deduced from this ：
  

Derive Bayesian theorem from conditional probability

See formula

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