As clinical drug development becomes more complex and resource-intensive, the FDA’s recent draft guidance on the use of Bayesian statistical methods in clinical trials signals a move toward more ...

A new AI-powered framework could transform how astronomers measure the expansion of the Universe. By analyzing images of Type ...

Articulate the need for computational approaches, such as Markov chain Monte Carlo (MCMC) algorithms, to Bayesian inference. Implement various MCMC algorithms to find posterior distributions, ...

The Multi-source Probabilistic Inference (MUPI) research group studies statistical machine learning and artificial intelligence. We develop new methods and algorithms for coping with uncertainty in ...

Abstract: The conventional geo-electromagnetic data inversions are mostly based on gradient optimization methods. However, this type of method can only provide a single “optimal” inverse model under ...

Abstract: Sparse diagnosis techniques for antenna arrays provide an efficient approach to fault diagnosis by leveraging the sparse nature of faulty elements. In practical scenarios, an unknown ...

Food System Innovations has launched an open-source Food Intelligence Lab to use AI to create better-tasting alternative ...

Neither Sakana AI nor its external AI service providers will use customer data or inputs for model training or fine-tuning unless the client provides explicit opt-in consent.

Bayesian methods are becoming an increasingly popular approach to data analysis across a wide range of research fields. They offer a flexible and structured framework for statistical inference, ...

Asteroid Vesta’s battered surface is far from static, it quietly preserves the memory of past collapses, impacts, and ...

Stats expert Tim Swartz uses 'spatiotemporal data' to figure out which throw-ins, corners and free kicks lead to wins ...

Learning from potential disinformation introduces specific cognitive biases, causing individuals to systematically deviate from an idealized Bayesian updating strategy.