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Concept

Relative likelihood

Publications associées (22)

Boosting likelihood learning with event reweighting

Andrea Wulzer, Alfredo Glioti, Siyu Chen

Extracting maximal information from experimental data requires access to the likelihood function, which however is never directly available for complex experiments like those performed at high energy colliders. Theoretical predictions are obtained in this ...

Springer Nature2024

Population pharmacokinetic model selection assisted by machine learning

Jan Sickmann Hesthaven

A fit-for-purpose structural and statistical model is the first major requirement in population pharmacometric model development. In this manuscript we discuss how this complex and computationally intensive task could benefit from supervised machine learni ...

SPRINGER/PLENUM PUBLISHERS2022

Social Learning Under Inferential Attacks

Ali H. Sayed, Stefan Vlaski, Virginia Bordignon, Konstantinos Ntemos

A common assumption in the social learning literature is that agents exchange information in an unselfish manner. In this work, we consider the scenario where a subset of agents aims at driving the network beliefs to the wrong hypothesis. The adversaries a ...

IEEE2021

Gaussian Process Regression for Maximum Entropy Distribution

Mohammadhossein Gorji, Mohsen Sadr

Maximum-Entropy Distributions offer an attractive family of probability densities suitable for moment closure problems. Yet finding the Lagrange multipliers which parametrize these distributions, turns out to be a computational bottleneck for practical clo ...

2020

Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation

Daniel Kuhn, Wolfram Wiesemann, Viet Anh Nguyen, Soroosh Shafieezadeh Abadeh

The likelihood function is a fundamental component in Bayesian statistics. However, evaluating the likelihood of an observation is computationally intractable in many applications. In this paper, we propose a non-parametric approximation of the likelihood ...

2019

Contributions to Likelihood-Based Modelling of Extreme Values

Léo Raymond-Belzile

xtreme value analysis is concerned with the modelling of extreme events such as floods and heatwaves, which can have large impacts. Statistical modelling can be useful to better assess risks even if, due to scarcity of measurements, there is inherently ver ...

EPFL2019

Gaussian Process Regression for Maximum Entropy Distribution

Mohsen Sadr

2019

Comparing methodologies for structural identification and fatigue life prediction of a highway bridge

Ian Smith, Alain Nussbaumer, Sai Ganesh Sarvotham Pai

Accurate measurement-data interpretation leads to increased understanding of structural behavior and enhanced asset-management decision making. In this paper, four data-interpretation methodologies, residual minimization, traditional Bayesian model updatin ...

2018

A guideline for the validation of likelihood ratio methods used for forensic evidence evaluation

Rudolf Haraksim

This Guideline proposes a protocol for the validation of forensic evaluation methods at the source level, using the Likelihood Ratio framework as defined within the Bayes' inference model. In the context of the inference of identity of source, the Likeliho ...

Elsevier Ireland Ltd2017

Modelling across extremal dependence classes

Anthony Christopher Davison, Jennifer Lynne Wadsworth

Different dependence scenarios can arise in multivariate extremes, entailing careful selection of an appropriate class of models. In bivariate extremes, the variables are either asymptotically dependent or are asymptotically independent. Most available sta ...

Wiley-Blackwell2017

MAGIC observations of the February 2014 flare of 1ES 1011+496 and ensuing constraint of the EBL density

Thomas Günther Bretz

Context. During February-March 2014, the MAGIC telescopes observed the high-frequency peaked BL Lac 1ES 1011 + 496 (z = 0.212) in flaring state at very-high energy (VHE, E > 100 GeV). The flux reached a level of more than ten times higher than any previous ...

Edp Sciences S A2016

Bayesian Inference For The Brown-Resnick Process, With An Application To Extreme Low Temperatures

Anthony Christopher Davison, Emeric Rolland Georges Thibaud

The Brown-Resnick max-stable process has proven to be well suited for modeling extremes of complex environmental processes, but in many applications its likelihood function is intractable and inference must be based on a composite likelihood, thereby preve ...

Inst Mathematical Statistics2016

LLR-Based Successive Cancellation List Decoding of Polar Codes

Andreas Peter Burg, Alexios Konstantinos Balatsoukas Stimming

We show that successive cancellation list decoding can be formulated exclusively using log-likelihood ratios. In addition to numerical stability, the log-likelihood ratio based formulation has useful properties that simplify the sorting step involved in su ...

Institute of Electrical and Electronics Engineers2015

Likelihood Estimation for the INAR(p) Model by Saddlepoint Approximation

Anthony Christopher Davison

Saddlepoint techniques have been used successfully in many applications, owing to the high accuracy with which they can approximate intractable densities and tail probabilities. This article concerns their use for the estimation of high-order integer-value ...

American Statistical Association2015

Accurate Directional Inference for Vector Parameters in Linear Exponential Families

Anthony Christopher Davison

We consider inference on a vector-valued parameter of interest in a linear exponential family, in the presence of a finite-dimensional nuisance parameter. Based on higher-order asymptotic theory for likelihood, we propose a directional test whose p-value i ...

American Statistical Association2014

Score Calibration in Face Recognition

Sébastien Marcel

This paper presents an evaluation of verification and calibration performance of a face recognition system based on inter-session variability modeling. As an extension to the calibration through linear transformation of scores, categorical calibration is i ...

2014

Efficient inference for spatial extreme value processes associated to log-Gaussian random functions

Jennifer Lynne Wadsworth

Max-stable processes arise as the only possible nontrivial limits for maxima of affinely normalized identically distributed stochastic processes, and thus form an important class of models for the extreme values of spatial processes. Until recently, infere ...

Oxford University Press2014

From pointwise testing to a regional vision: An integrated statistical approach to detect nonstationarity in extreme daily rainfall. Application to the Sahelian region

Juliette Ferial Blanchet

Global warming is expected to intensify the hydrologic cycle. Documenting whether significant changes in the extreme precipitation regimes have already happened is consequently one of the challenging topics in climatic research. The high natural variabilit ...

Wiley-Blackwell2013

Unified Framework of Feature Based Adaptation for Statistical Speech Synthesis and Recognition

Lakshmi Babu Saheer

The advent of statistical parametric speech synthesis has paved new ways to a unified framework for hidden Markov model (HMM) based text to speech synthesis (TTS) and automatic speech recognition (ASR). The techniques and advancements made in the field of ...

EPFL2013

Oversampled Noisy Binary Image Sensor

Martin Vetterli, Feng Yang

We study the oversampled binary image sensor in [1] under noisy scenario. The binary image sensor is similar to traditional photographic film with pixel value equal to "0" or "1". The potential application of the oversampled binary image sensor is high dyn ...

Ieee2013