viaBayesian

viaBayesian is a probabilistic inference framework designed to combine prior knowledge with observed data using Bayesian methods. It treats model parameters and latent quantities as random variables and represents uncertainty with probability distributions rather than point estimates. The framework emphasizes modularity and interpretability, enabling users to specify likelihood models, priors, and hierarchical structure in a coherent, end-to-end pipeline.

Core components include prior elicitation, likelihood specification, and posterior computation. In viaBayesian approaches, the central goal is to obtain the posterior distribution p(theta|D) ∝ p(D|theta) p(theta) and to use the posterior for uncertainty quantification and predictive checks. Inference is typically performed by Monte Carlo sampling, variational methods, or streaming updates for online data. The framework supports model comparison and posterior predictive assessment to evaluate fit.

Applications span science and engineering, finance, epidemiology, and data science, where calibrated uncertainty is essential. It underpins Bayesian experimental design, decision making under uncertainty, and robust forecasting. It is closely related to probabilistic programming and Bayesian networks.

History and status: viaBayesian is a descriptive label used in various academic writings and software projects; there is no single canonical implementation or standard definition. As with other Bayesian methods, practical use requires careful prior specification, model checking, and consideration of computational cost.

See also: Bayesian inference, probabilistic programming, Bayesian networks, Markov chain Monte Carlo, variational inference.