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.
pdf(probability density function) and cdf(cumulative density function) of Gaussian distribution
Sum (or 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
And it’s common to calculate the 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
