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This paper introduces a Bayesian framework to detect multiple signals embedded in noisy observations from a sensor array. For various states of knowledge on the communication channel and the noise at the receiving sensors, a marginalization…

Information Theory · Computer Science 2009-09-08 Romain Couillet , Merouane Debbah

This paper offers a qualitative insight into the convergence of Bayesian parameter inference in a setup which mimics the modeling of the spread of a disease with associated disease measurements. Specifically, we are interested in the…

Statistics Theory · Mathematics 2022-12-08 Samuel Bronstein , Stefan Engblom , Robin Marin

We address the need to generate faithful explanations of "black box" Deep Learning models. Several tests have been proposed to determine aspects of faithfulness of explanation methods, but they lack cross-domain applicability and a rigorous…

Machine Learning · Computer Science 2023-06-27 Harshinee Sriram , Cristina Conati

We characterize different cell states, related to cancer and ageing phenotypes, by a measure of entropy of network ensembles, integrating gene expression values and protein interaction networks. The entropy measure estimates the parameter…

Molecular Networks · Quantitative Biology 2013-05-24 G. Menichetti , G. Bianconi , E. Giampieri , G. Castellani , D. Remondini

There is growing interest in explainable recommender systems that provide recommendations along with explanations for the reasoning behind them. When evaluating recommender systems, most studies focus on overall recommendation performance.…

Information Retrieval · Computer Science 2025-07-03 Yeonbin Son , Matthew L. Bolton

Clinical biosensors with low detection limit hold significant promise in the early diagnosis of debilitating diseases. Recent progress in sensor development has led to the demonstration of detection capable of detecting target molecules…

Medical Physics · Physics 2019-12-05 Tuhin Chakrabortty , Chandra R. Murthy , Manoj Varma

Deep learning is a topic of considerable current interest. The availability of massive data collections and powerful software resources has led to an impressive amount of results in many application areas that reveal essential but hidden…

Machine Learning · Computer Science 2023-01-31 Gianluigi Pillonetto , Aleksandr Aravkin , Daniel Gedon , Lennart Ljung , Antônio H. Ribeiro , Thomas B. Schön

We propose a geometric framework to assess sensitivity of Bayesian procedures to modeling assumptions based on the nonparametric Fisher-Rao metric. While the framework is general in spirit, the focus of this article is restricted to…

Methodology · Statistics 2014-04-28 Sebastian Kurtek , Karthik Bharath

Subjective judgements from experts provide essential information when assessing and modelling threats in respect to cyber-physical systems. For example, the vulnerability of individual system components can be described using multiple…

Cryptography and Security · Computer Science 2019-10-03 Zack Ellerby , Josie McCulloch , Melanie Wilson , Christian Wagner

Networks are complex models for underlying data in many application domains. In most instances, raw data is not natively in the form of a network, but derived from sensors, logs, images, or other data. Yet, the impact of the various choices…

Social and Information Networks · Computer Science 2020-04-07 Ivan Brugere , Tanya Y. Berger-Wolf

Hierarchical Bayesian models are increasingly used in large, inhomogeneous complex network dynamical systems by modeling parameters as draws from a hyperparameter-governed distribution. However, theoretical guarantees for these estimates as…

Statistics Theory · Mathematics 2026-01-23 Yi Yu , Yubo Hou , Yinchong Wang , Nan Zhang , Jianfeng Feng , Wenlian Lu

In recent times, neural networks have become a powerful tool for the analysis of complex and abstract data models. However, their introduction intrinsically increases our uncertainty about which features of the analysis are model-related…

Machine Learning · Statistics 2020-11-09 Tom Charnock , Laurence Perreault-Levasseur , François Lanusse

Decision-making is a cognitively intensive task that requires synthesizing relevant information from multiple unstructured sources, weighing competing factors, and incorporating subjective user preferences. Existing methods, including large…

Computation and Language · Computer Science 2026-04-21 Akriti Jain , Anish Mulay , Divyansh Verma , Aishani Pandey , Pritika Ramu , Aparna Garimella

Radar sensors play a crucial role for perception systems in automated driving but suffer from a high level of noise. In the past, this could be solved by strict filters, which remove most false positives at the expense of undetected…

Signal Processing · Electrical Eng. & Systems 2025-07-08 Tim Brühl , Jenny Glönkler , Robin Schwager , Tin Stribor Sohn , Tim Dieter Eberhardt , Sören Hohmann

With new advancements in technology, it is now possible to collect data for a variety of different metrics describing tumor growth, including tumor volume, composition, and vascularity, among others. For any proposed model of tumor growth…

Quantitative Methods · Quantitative Biology 2020-09-08 Heyrim Cho , Allison L. Lewis , Kathleen M. Storey

High dimensional categorical data are routinely collected in biomedical and social sciences. It is of great importance to build interpretable parsimonious models that perform dimension reduction and uncover meaningful latent structures from…

Methodology · Statistics 2023-01-31 Yuqi Gu , David B. Dunson

Motivation: Identifying the molecular pathways more prone to disruption during a pathological process is a key task in network medicine and, more in general, in systems biology. Results: In this work we propose a pipeline that couples a…

The mutations of a complex systemic disease like cancer can be modeled as stuck-at faults in the Boolean system paradigm. For a class of multiple faults, the fault identification is exceptionally significant under the incomplete access of…

Systems and Control · Computer Science 2018-09-11 Anuj Deshpande , Ritwik Kumar Layek

Directed graphical models such as Bayesian nets are often used to implement intelligent tutoring systems able to interact in real-time with learners in a purely automatic way. When coping with such models, keeping a bound on the number of…

Artificial Intelligence · Computer Science 2024-09-10 Alessandro Antonucci , Francesca Mangili , Claudio Bonesana , Giorgia Adorni

In causal inference, sensitivity analysis is important to assess the robustness of study conclusions to key assumptions. We perform sensitivity analysis of the assumption that missing outcomes are missing completely at random. We follow a…

Statistics Theory · Mathematics 2023-05-12 Bart Eggen , Stéphanie L. van der Pas , Aad W. van der Vaart