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, ...

Abstract: In this paper, we study the stochastic state trajectory and conductance distributions of memristors under periodic pulse excitation. Our results, backed by experimental evidence, reveal that ...

Abstract: Many complex dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes. We consider two such models: the switching linear ...

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 ...

As clinical drug development becomes more complex and resource-intensive, the FDA’s recent draft guidance on the use of Bayesian statistical methods in clinical trials signals a move toward more ...

Overview. Data transformations are a useful companion for parametric regression models. A well-chosen or learned transformation can greatly enhance the applicability of a given model, especially for ...

2 College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China Objective To demonstrate an application of Bayesian model averaging (BMA) with generalised additive ...

Keep the news in the Wayback Machine. Sign Fight for the Future's letter. An icon used to represent a menu that can be toggled by interacting with this icon. A line drawing of the Internet Archive ...

A complete list of papers about adversarial examples It appears that the List of All Adversarial Example Papers has been experiencing crashes over the past few days. In the absence of this valuable ...

Find out more about undergraduate study at the School of Electronic Engineering and Computer Science.

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