Publications associées à Loi multinomiale

Extensions of Peer Prediction Incentive Mechanisms

As large, data-driven artificial intelligence models become ubiquitous, guaranteeing high data quality is imperative for constructing models. Crowdsourcing, community sensing, and data filtering have long been the standard approaches to guaranteeing or imp ...

EPFL2024

Wideband integrated bioaerosol sensor (WIBS) excited, fluorescent, and hyper-fluorescent particle number concentrations and normalized size distributions (dN/dlogDp) measured in the Swiss container during MOSAiC 2019/2020

Julia Schmale, Ivo Fabio Beck, Alireza Moallemi, Margarida Teles Nogueira Rolo

These datasets contain the total particle number concentrations and normalized size distributions (dN/dlogDp) of excited, fluorescent, and hyper-fluorescent particles of sizes 0.5 to 20 μm (optical diameter). The normalized size distribution datasets are s ...

Zenodo2023

Sub‐asymptotic motivation for new conditional multivariate extreme models

Anthony Christopher Davison, Thomas Lugrin, Jonathan A. Tawn

Statistical models for extreme values are generally derived from non-degenerate probabilistic limits that can be used to approximate the distribution of events that exceed a selected high threshold. If convergence to the limit distribution is slow, then th ...

2021

Impact of synchrophasor measurement types and uncertainties on the accuracy of distribution system linear state estimators

Mario Paolone, Lorenzo Zanni, Sidi-Rachid Cherkaoui, Daniele Colangelo

The paper aims at assessing the effects of combined voltage and/or current synchrophasor measurements, and their associated uncertainties, on the accuracy of state estimators adopted in distribution systems. Such an assessment is first carried out with res ...

IEEE2015

A Collaborative Sensor Fusion Algorithm for Multi-Object Tracking Using a Gaussian Mixture Probability Hypothesis Density Filter

Alcherio Martinoli, Milos Vasic

This paper presents a method for collaborative tracking of multiple vehicles that extends a Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter with a collaborative fusion algorithm. Measurements are preprocessed in a detect-before-track fashio ...

2015

Sparsity and Infinite Divisibility

Michaël Unser, Arash Amini

We adopt an innovation-driven framework and investigate the sparse/compressible distributions obtained by linearly measuring or expanding continuous-domain stochastic models. Starting from the first principles, we show that all such distributions are neces ...

2014

Saliency-based representations and multi-component classifiers for visual scene recognition

Marco Fornoni

Visual scene recognition deals with the problem of automatically recognizing the high-level semantic concept describing a given image as a whole, such as the environment in which the scene is occurring (e.g. a mountain), or the event that is taking place ( ...

EPFL2014

Proof of a conjecture by Gazeau et al. using the Gould-Hopper polynomials

Olivier Lévêque

We provide here some probabilistic interpretations of the generalized binomial distributions proposed by Gazeau et al. ["Generating functions for generalized binomial distributions," J. Math. Phys. 53, 103304 (2012)]. In the second part, we prove the "stro ...

Amer Inst Physics2013

A new representation for multivariate tail probabilities

Jennifer Lynne Wadsworth, Jonathan A. Tawn

Existing theory for multivariate extreme values focuses upon characterizations of the distributional tails when all components of a random vector, standardized to identical margins, grow at the same rate. In this paper, we consider the effect of allowing t ...

Bernoulli Society for Mathematical Statistics and Probability2013

The Analog Formulation of Sparsity Implies Infinite Divisibility and Rules Out Bernoulli-Gaussian Priors

Michaël Unser, Arash Amini, Ulugbek Kamilov

Motivated by the analog nature of real-world signals, we investigate continuous-time random processes. For this purpose, we consider the stochastic processes that can be whitened by linear transformations and we show that the distribution of their samples ...

Ieee2012