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 ...

Abstract: This paper presents a comparative analysis of three sampling techniques for generating space points to develop a Bayesian neural network (BNN) surrogate model of a synthetic second-order low ...

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Abstract: Bayesian neural networks (BNNs), which can represent predictive uncertainty, have been an essential framework for reliable machine learning. Unfortunately, BNNs are difficult to use in ...

Neurons in the cortex show irregular activity with and without explicit stimuli. Theoretical modeling provides a persuading explanation of such complex dynamics using chaos—however, little is known ...

Purpose: Bayesian calibration is generally superior to standard direct-search algorithms in that it estimates the full joint posterior distribution of the calibrated parameters. However, there are ...

Bayesian Networks are graphical models useful for various applications, including time series prediction and anomaly detection. Bayesian inference offers a robust set of tools for modelling ...

Genetic recombination processes, such as reassortment, make it complex or impossible to use standard phylogenetic and phylodynamic methods. This is due to the fact that the shared evolutionary history ...