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We introduce a simple time-triggered protocol to achieve communication-efficient non-Bayesian learning over a network. Specifically, we consider a scenario where a group of agents interact over a graph with the aim of discerning the true…

Systems and Control · Electrical Eng. & Systems 2019-09-05 Aritra Mitra , John A. Richards , Shreyas Sundaram

This paper focuses on the convergence of infor- mation in distributed systems of agents communicating over a network. The information on which the convergence is sought is not represented by real numbers, rather by sets of real numbers,…

Robotics · Computer Science 2011-01-13 Adriano Fagiolini , Nevio Dubbini , Antonio Bicchi

In this paper I empirically investigate prediction markets for binary options. Advocates of prediction markets have suggested that asset prices are consistent estimators of the "true" probability of a state of the world being realized. I…

Economics · Quantitative Finance 2016-09-13 Joachim R. Groeger

We consider the estimation of a signal from the knowledge of its noisy linear random Gaussian projections. A few examples where this problem is relevant are compressed sensing, sparse superposition codes, and code division multiple access.…

Information Theory · Computer Science 2020-08-31 Jean Barbier , Nicolas Macris , Mohamad Dia , Florent Krzakala

Complex decision-making systems rarely have direct access to the current state of the world and they instead rely on opinions to form an understanding of what the ground truth could be. Even in problems where experts provide opinions…

Artificial Intelligence · Computer Science 2023-08-22 Noyan C. Sevuktekin , Andrew C. Singer

In the consensus model with bounded confidence, studied by Deffuant et al. (2000), two randomly selected people who differ not too much in their opinion both shift their opinions towards each other. Now we restrict this exchange of…

Statistical Mechanics · Physics 2009-11-10 D. Stauffer , H. Meyer-Ortmanns

We consider the problem of subspace estimation in situations where the number of available snapshots and the observation dimension are comparable in magnitude. In this context, traditional subspace methods tend to fail because the…

Information Theory · Computer Science 2016-11-15 Pascal Vallet , Philippe Loubaton , Xavier Mestre

A distributed consensus algorithm for estimating the maximum value of the initial measurements in a sensor network with communication noise is proposed. In the absence of communication noise, max estimation can be done by updating the state…

Systems and Control · Computer Science 2016-02-04 Sai Zhang , Cihan Tepedelenlioglu , Mahesh K. Banavar , Andreas Spanias

To obtain reliable results of expertise, which usually use individual and group expert pairwise comparisons, it is important to summarize (aggregate) expert estimates provided that they are sufficiently consistent. There are several ways to…

Methodology · Statistics 2024-10-07 Vitaliy Tsyganok , Andriy Olenko , Pavlo Roik , Oksana Vlasenko

Sparse MoE routing fails at domain transitions, where the current token belongs to one distribution and the next to another. In a controlled experiment (4 experts, 5 seeds), standard affinity routing assigns only 0.006 +/- 0.001 probability…

Machine Learning · Computer Science 2026-05-04 Man Yung Wong

This paper deals with the parametric inference for integrated signals embedded in an additive Gaussian noise and observed at deterministic discrete instants which are not necessarily equidistant. The unknown parameter is multidimensional…

Statistics Theory · Mathematics 2019-03-18 Dominique Dehay , Khalil El Waled , Vincent Monsan

This paper characterizes the conditions under which the observed beliefs of a group of agents are consistent with Bayesian updating. Beliefs are consistent with Bayesianism if they arise from the application of Bayes' rule given some…

Theoretical Economics · Economics 2024-01-19 Pooya Molavi

Motivated by the recent interest in approximate message passing (AMP) for matrix-valued linear observations with superposition of \emph{multiple statistically asymmetric signal sources}, we introduce a multi-source AMP framework in which…

Information Theory · Computer Science 2025-01-22 Burak Çakmak , Giuseppe Caire

Mutual information is widely used, in a descriptive way, to measure the stochastic dependence of categorical random variables. In order to address questions such as the reliability of the descriptive value, one must consider…

Machine Learning · Computer Science 2007-07-13 Marcus Hutter , Marco Zaffalon

Bayesian error analysis paves the way to the construction of credible and plausible error regions for a point estimator obtained from a given dataset. We introduce the concept of region accuracy for error regions (a generalization of the…

Quantum Physics · Physics 2019-07-15 Changhun Oh , Yong Siah Teo , Hyunseok Jeong

We propose a stochastic model of opinion exchange in networks. A finite set of agents is organized in a fixed network structure. There is a binary state of the world and each agent receives a private signal on the state. We model beliefs as…

Theoretical Economics · Economics 2022-01-31 Emilien Macault

Confirmation bias, the tendency to interpret information in a way that aligns with one's preconceptions, can profoundly impact scientific research, leading to conclusions that reflect the researcher's hypotheses even when the observational…

Machine Learning · Statistics 2025-09-09 Amnon Balanov , Tamir Bendory , Wasim Huleihel

In the compromise model of Deffuant et al., opinions are real numbers between 0 and 1 and two agents are compatible if the difference of their opinions is smaller than the confidence bound parameter \epsilon. The opinions of a randomly…

Statistical Mechanics · Physics 2009-11-10 Santo Fortunato

The setting of a right-censored random sample subject to contamination is considered. In various fields, expert information is often available and used to overcome the contamination. This paper integrates expert knowledge into the…

Methodology · Statistics 2023-03-28 Martin Bladt , Christian Furrer

In Bayesian inference, we seek to compute information about random variables such as moments or quantiles on the basis of {available data} and prior information. When the distribution of random variables is {intractable}, Monte Carlo (MC)…

Statistics Theory · Mathematics 2021-04-06 Alec Koppel , Amrit Singh Bedi , Brian M. Sadler , Victor Elvira