Everybody knows about Gaussian distribution, and Gaussian is very popular in Bayesian world and even in our life. This article summaries typical operation of Gaussian, and something about Truncated Guassian distribution.

## Gaussian

### pdf and cdf

### Sum/substraction of two independent Gaussian random variables

Please take care upper formula only works when x1 and x2 are independent. And it’s easy to get the distribution for variable x=x1-x2 See here for the detils of inference

### Product of two Gaussian pdf

Please take care x is no longer a gaussian distribution. And you can find it’s very elegant to use ‘precision’ and ‘precision adjusted mean’ for Gaussian operation like multiply and division. See here for the detils of inference

### Division of two Gaussian pdf

### Intergral of the product of two gaussian distribution

## Truncated Gaussian

Truncated Gaussian distribution is very simple: it’s just one conditional (Gaussian) distribution. Suppose variable x belongs to Gaussian distribution, then x conditional on x belongs to (a, b) has a truncated Gaussian distribution.

### Calculate expectation of Truncated Gaussian

### Calculate variance of Truncated Gaussian