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Stochastic Approach to Data Analysis in Fluorescence Correlation Spectroscopy

Related publications (59)

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Sparsity-Driven Statistical Inference for Inverse Problems

Ulugbek Kamilov

This thesis addresses statistical inference for the resolution of inverse problems. Our work is motivated by the recent trend whereby classical linear methods are being replaced by nonlinear alternatives that rely on the sparsity of naturally occurring sig ...

EPFL2015

Estimation of Space-Time Varying Parameters Using a Diffusion LMS Algorithm

Ali H. Sayed

We study the problem of distributed adaptive estimation over networks where nodes cooperate to estimate physical parameters that can vary over both space and time domains. We use a set of basis functions to characterize the space-varying nature of the para ...

2014

Population Sensing Using Mobile Devices

Farid Movahedi Naini

In our daily lives, our mobile phones sense our movements and interactions via a rich set of embedded sensors such as a GPS, Bluetooth, accelerometers, and microphones. This enables us to use mobile phones as agents for collecting spatio-temporal data. The ...

EPFL2014

MATHICSE Technical Report : A continuation multilevel Monte Carlo algorithm

Fabio Nobile, Erik Gustaf Bogislaw Von Schwerin

We propose a novel Continuation Multi Level Monte Carlo (CMLMC) algorithm for weak approximation of stochastic models that are described in terms of differential equations either driven by random measures or with random coefficients. The CMLMC algorithm so ...

MATHICSE2014

Statistical Inference in Positron Emission Tomography

Matthieu Martin Jean-André Simeoni

In this report, we investigate mathematical algorithms for image reconstruction in the context of positron emission tomography (a medical diagnosis technique). We first take inspiration from the physics of PET to design a mathematical model tailored to the ...

2014

Performance analysis of multitask diffusion adaptation over asynchronous networks

Ali H. Sayed, Roula Nassif

The multitask diffusion LMS algorithm is an efficient strategy to address distributed estimation problems that are multitask-oriented in the sense that the optimum parameter vector may not be the same for every cluster of nodes. In this work, we explore th ...

IEEE2014

Bayesian Estimation for Continuous-Time Sparse Stochastic Processes

Michaël Unser, Arash Amini, Emrah Bostan, Ulugbek Kamilov

We consider continuous-time sparse stochastic processes from which we have only a finite number of noisy/noiseless samples. Our goal is to estimate the noiseless samples (denoising) and the signal in-between (interpolation problem). By relying on tools fro ...

Ieee-Inst Electrical Electronics Engineers Inc2013

A Recommender System Based on Belief Propagation over Pairwise Markov Random Fields

Erman Ayday

Recommender systems enable service providers to predict and address the individual needs of their customers so as to deliver personalized experiences. In this paper, we formulate the recommendation problem as an inference problem on a Pairwise Markov Rando ...

Ieee2013

An algorithm for automatic inertial sensor calibration

Jan Skaloud, Stéphane Guerrier

We present an algorithm for determining the nature of stochastic processes together with its parameters based on the analysis of time series of inertial errors. The algorithm is suitable mainly (but not only) for situations when several stochastic processe ...

2013

Cooperative off-policy prediction of Markov decision processes in adaptive networks

Ali H. Sayed

We apply diffusion strategies to propose a cooperative reinforcement learning algorithm, in which agents in a network communicate with their neighbors to improve predictions about their environment. The algorithm is suitable to learn off-policy even in lar ...

IEEE2013