LIBSi

LIBSi is a software library for performing Bayesian inference. It is designed to be flexible and efficient, supporting a variety of probabilistic models and inference algorithms. The library is written in Python and can be used for tasks such as parameter estimation, model selection, and uncertainty quantification.

The core of LIBSi lies in its ability to represent and manipulate probability distributions. It provides tools for defining custom probability models using standard Python constructs, allowing users to express complex dependencies between variables. For inference, LIBSi offers implementations of several popular algorithms, including Markov Chain Monte Carlo (MCMC) methods like Metropolis-Hastings and Gibbs sampling, as well as variational inference techniques.

LIBSi aims to make Bayesian inference accessible to a broader audience by providing a user-friendly interface. It integrates well with other scientific Python libraries such as NumPy and SciPy, facilitating data manipulation and analysis. The library is actively developed, with ongoing efforts to improve performance, expand the range of supported models and algorithms, and enhance documentation. It serves as a valuable resource for researchers and practitioners in fields that require probabilistic modeling and statistical inference.