Pattern Recognition - Chapter 6: Bayesian parameter estimation
1. Introduce Bayesian parameter estimation (Maximum-A-posterior) 2. An example 2.1. The pre-observation 2.2. The post-observation...
Review on Pattern Recognition Chapter 5: Maximum-Likelihood estimation
Chapter 5 is very complicated to understand. That is why I publish this blog to summarize the knowledge in the Chapter 5. Basically, we...
Pattern Recognition - Chapter 5: Maximum-Likelihood estimation
1. Maximum-Likelihood estimate (MLE) Equation (1) (*Source: Richard O.Duda et al. Pattern Recognition) Equation (2) Equation (3) 2. MLE...
Pattern Recognition - Chapter 4: Discriminant function with Gaussian distribution
Equation (1) 1. Independent features with the same (common) covariance (*Source: Tso B. and Mather P. Classification Methods for Remotely...
Pattern Recognition - Chapter 3: Bayes decision rule
(*Source: Richard O.Duda et al. Pattern Recognition) 1. Bayes decision rule 1.1. Risk function 1.1.1. Simple cost function (*Source:...
Pattern Recognition - Chapter 2: Normal distribution
1. Normal distribution (*Source: https://en.wikipedia.org/wiki/Normal_distribution) 2. Multivariate normal densities (*Source:...
Pattern Recognition - Chapter 1: Basic probability theory
1. Discrete - Continuous random variable 1.1. Discrete random variable 1.2. Continuous random variable 2. Statistical independence 2.1....
