Bayesian regression with linear basis function models. Introduction to Bayesian linear regression. Implementation with plain NumPy and scikit-learn. See also PyMC3 implementation. Gaussian processes.

Credit risk modelling is a cornerstone of modern finance, enabling lenders to quantify the risk that a borrower will default on their obligations. One of the most important metrics in this domain is ...

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A regression problem is one where the goal is to predict a single numeric value. For example, you might want to predict the price of a house based on its square footage, age, number of bedrooms and ...

Bayesian estimation is a statistical technique used to update our beliefs about a hypothesis based on new evidence. It is a probabilistic approach to statistical inference which involves calculating ...

Predictive microbiology models explain bacterial number variations over time and how growth/inactivation rates are affected by environmental conditions (Lammerding and Fazil, 2000). In the development ...

For high-throughput screening of materials for heterogeneous catalysis, scaling relations provides an efficient scheme to estimate the chemisorption energies of hydrogenated species. However, ...

Polygenic risk scores (PRS), which summarize the effects of genome-wide genetic markers to measure the genetic liability to a trait or a disorder, have shown promise in predicting human complex traits ...

To streamline the interpretation of vibrational spectra, this work introduces the use of Bayesian linear regression with automatic relevance determination as a viable approach to decompose the atomic ...

This repository contains Python/PyMC3 code for a selection of models and figures from the book 'Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan', Second Edition, by John Kruschke (2015 ...