Explore predictive modeling for compound prioritization, including in silico screening, toxicology models, and lead selection ...

Abstract: Machine learning (ML) is widely used in intelligent software systems. However, the uncertain outputs from ML models can lead to undesirable consequences in safety-critical applications. To ...

Imagine a scenario where a team of doctors faces a perplexing medical puzzle. A patient shows a range of symptoms, each pointing to multiple possible diseases. How can they navigate this diagnostic ...

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.

In this blog post, I am going to teach you how to train a Bayesian deep learning classifier using Keras and tensorflow. Before diving into the specific training example, I will cover a few important ...

Experimental variogram modelling is an essential process in geostatistics. The use of artificial intelligence (AI) is a new and advanced way of automating experimental variogram modelling. One part of ...

Identifying lithology is crucial for geological exploration, and the adoption of artificial intelligence is progressively becoming a refined approach to automate this process. A key feature of this ...

Abstract: Bayesian networks are important Machine Learning models with many practical applications in, e.g., biomedicine and bioinformatics. The problem of Bayesian networks learning is $\mathcal ...