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Publications associées (10)

Fast Bayesian estimation of spatial count data models

Spatial count data models are used to explain and predict the frequency of phenomena such as traffic accidents in geographically distinct entities such as census tracts or road segments. These models are typically estimated using Bayesian Markov chain Mont ...

2021

Quantifying the Effects of Contact Tracing, Testing, and Containment Measures in the Presence of Infection Hotspots

William Trouleau

Multiple lines of evidence at the individual and population level strongly suggest that infection hotspots, or superspreading events, where a single individual infects many others, play a key role in the transmission dynamics of COVID-19. However, most of ...

2020

Generating Sparse Stochastic Processes Using Matched Splines

Michaël Unser, Leello Tadesse Dadi, Shayan Aziznejad

We provide an algorithm to generate trajectories of sparse stochastic processes that are solutions of linear ordinary differential equations driven by Levy white noises. A recent paper showed that these processes are limits in law of generalized compound-P ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Wavelet analysis of the Besov regularity of Levy white noise

Julien René Pierre Fageot, Shayan Aziznejad

We characterize the local smoothness and the asymptotic growth rate of the Levy white noise. We do so by characterizing the weighted Besov spaces in which it is located. We extend known results in two ways. First, we obtain new bounds for the local smoothn ...

2020

Discrimination of numerical proportions: A comparison of binomial and Gaussian models

Aire Raidvee

Observers discriminated the numerical proportion of two sets of elements (N = 9, 13, 33, and 65) that differed either by color or orientation. According to the standard Thurstonian approach, the accuracy of proportion discrimination is determined by irredu ...

Springer2017

Applications of Approximate Learning and Inference for Probabilistic Models

Young Jun Ko

We develop approximate inference and learning methods for facilitating the use of probabilistic modeling techniques motivated by applications in two different areas. First, we consider the ill-posed inverse problem of recovering an image from an underdeter ...

EPFL2017

Expectation Propagation for Rectified Linear Poisson Regression

Matthias Seeger, Young Jun Ko

The Poisson likelihood with rectified linear function as non-linearity is a physically plausible model to discribe the stochastic arrival process of photons or other particles at a detector. At low emission rates the discrete nature of this process leads t ...

2015

Three Examples of Accurate Likelihood Inference

Anthony Christopher Davison

The modern theory of likelihood inference provides improved inferences in many parametric models, with little more effort than is required for application of standard first-order theory. We outline the relevant computations, and illustrate the calculations ...

American Statistical Association2010

A systematic comparison of continuous and discrete mixture models

Michel Bierlaire

Modellers are increasingly relying on the use of continuous random coefficients models, such as Mixed Logit, for the representation of variations in tastes across individuals. In this paper, we provide an in-depth comparison of the performance of the Mixed ...

2006

Comparison and Validation of Tissue Modelization and Statistical Classification Methods in T1-weighted MR Brain Images

Jean-Philippe Thiran, Meritxell Bach Cuadra, Leila Cammoun, Olivier Cuisenaire, Torsten Butz

This paper presents a validation study on statistical non-supervised brain tissue classification techniques in MR images. Several image models assuming different hypotheses regarding the intensity distribution model, the spatial model and the number of cla ...

2005

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