Author name: tirta

Bayesian machine learning is a powerful approach to machine learning that utilizes probability distributions to represent uncertainty. It allows for […]

Hierarchical Bayesian modeling is a powerful statistical approach that incorporates hierarchical structures into Bayesian models. It allows for modeling complex

Bayesian belief nets are graphical models that represent probabilistic relationships between variables. Nodes represent variables, arcs represent dependencies, and probability

Bayesian hierarchical models (BHMs) are statistical models that incorporate multiple levels of data and uncertainty. They extend traditional Bayesian models

Approximate Bayesian Computation (ABC) is a modeling technique that uses simulation to make inferences about complex models. It involves creating

Bayesian Optimization of Function Networks with Partial Evaluations explores the application of Bayesian optimization techniques to enhance the performance of

Dynamic Bayesian networks (DBNs) are a type of graphical model that represents temporal dependencies among variables. Nodes in a DBN

Bayesian optimization of function networks involves using Bayesian optimization, a powerful technique for optimizing hyperparameters and exploring models, to improve

Minutiae are detailed, unique features found in fingerprints, such as ridge endings, bifurcations, short ridges, inclusions, pores, fissures, and scars.

Substructure vs Superstructure: The substructure, consisting of the foundation, supports the weight of the building and transmits it to the

A cyclical structure is a pattern that repeats itself in a predictable sequence. It is characterized by entities that are

This blog post focuses on architectural structures that serve as landmarks. It examines buildings and temples, highlighting their architectural features