SOP For Performig BLR in Python
Posterior samples of a linear modelThis document serves as a standard operating procedure (SOP) for performing Bayesian linear regression (BLR). The goals of this document are as follows: users will be familiar with the mathematics and theory behind BLR, users will be able to implement a simple example of BLR in python, and users will be able to take this knowledge and extended it to their own personal modeling situa- tions. In order to use this document successfully users will need to be familiar with gradients and their minimization properties from calculus III. Along with this, a basic understanding of statistics is required. Users are expected to under- stand the properties of normal distributions and be able to find their mean and variance. Lastly a basic understanding of Python and specifically the packages Numpy and Matplotlib
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Harry Winston Sullivan
PhD Student and Bayesian Bro
I go by Winston or whatever you want to call me. If someone could explain to me how functional analysis, gaussian processes, quantum many body theory, or how to tie my own shoes that would great. Mathematics, the only hobby which somehow makes you both smarter and stupider at the same time.