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PyMultiNest provides programmatic access to MultiNest (ascl:1109.006) and PyCuba, integration existing Python code (numpy, scipy), and enables writing Prior & LogLikelihood functions in Python. PyMultiNest can plot and visualize MultiNest's progress and allows easy plotting, visualization and summarization of MultiNest results. The plotting can be run on existing MultiNest output, and when not using PyMultiNest for running MultiNest.
BXA connects the nested sampling algorithm MultiNest (ascl:1109.006) to the X-ray spectral analysis environments Xspec (ascl:9910.005) and Sherpa (ascl:1107.005) for Bayesian parameter estimation and model comparison. It provides parameter estimation in arbitrary dimensions and plotting of spectral model vs. the data for best fit, posterior samples, or each component. BXA allows for model selection; it computes the evidence for the considered model, ready for use in computing Bayes factors and is not limited to nested models. It also visualizes deviations between model and data with Quantile-Quantile (QQ) plots, which do not require binning and are more comprehensive than residuals.
This three-component package provides a Pythonic implementation of the Nested Sampling integration algorithm for Bayesian model comparison and parameter estimation. It offers multiple implementations for constrained drawing functions and a test suite to evaluate the correctness, accuracy and efficiency of various implementations. The three components are:
nway is a source cross-matching tool for arbitrarily many astronomical catalogs. It features Bayesian match probabilities based on astronomical sky coordinates (RA, DEC), works with arbitrarily many catalogs, and can handle varying errors. nway can also incorporate additional prior information, such as the magnitude or color distributions of the sources to match, and works accurately and fast in small areas and all-sky catalogs.