Bayesian Inference in Python

Observational astronomers don’t simply present images or spectra, we analyze the data and use it to support or contradict physical models. A key aspect of data analysis is understanding the certainty of claims that are made. Thus using statistics is a fundamental part of observational astronomy. Statistical inference is one method of drawing conclusions, and establishing [...]

CosmoMC Bayesian Inference Package - sampling posterior probability distributions of cosmological parameters.

Observational astronomers don’t simply present images or spectra, we analyze the data and use it to support or contradict physical models. A key aspect of data analysis is understanding the certainty of claims that are made. Thus using statistics is a fundamental part of observational astronomy.
Statistical inference is one method of drawing conclusions, and establishing their certainty, given a set of observational data that is subject to random variation. There are two broad categories of statistics in wide-spread use in astronomy: Frequentist and Bayesian statistics. The frequentist sees probability as the long-run expected frequency of occurrence while the Bayesian view of probability is related to degree of belief; it is a measure of the plausibility of an event given incomplete knowledge. I won’t go into the pros and cons of ...