Bayesian estimation of mixed multinomial logit models: Advances and simulation-based evaluations

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We consider the problem of defining and fitting models of autoregressive time series of probability distributions on a compact interval of Double-struck capital R. An order-1 autoregressive model in this context is to be understood as a Markov chain, where ...

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This thesis concerns the theory of positive-definite completions and its mutually beneficial connections to the statistics of function-valued or continuously-indexed random processes, better known as functional data analysis. In particular, it dwells upon ...

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Beliefs inform the behaviour of forward-thinking agents in complex environments. Recently, sequential Bayesian inference has emerged as a mechanism to study belief formation among agents adapting to dynamical conditions. However, we lack critical theory to ...

A kernel method for estimating a probability density function from an independent and identically distributed sample drawn from such density is presented. Our estimator is a linear combination of kernel functions, the coefficients of which are determined b ...

The increasing availability of sensing techniques provides a great opportunity for engineers to design state estimation methods, which are optimal for the system under observation and the observed noise patterns. However, these patterns often do not fulfil ...

Cells are the smallest operational units of living systems. Through synthesis of various biomolecules and exchange of signals with the environment, cells tightly regulate their composition to realize a specific functional state. The transformation of a cel ...

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Higher-order asymptotics provide accurate approximations for use in parametric statistical modelling. In this thesis, we investigate using higher-order approximations in two-specific settings, with a particular emphasis on the tangent exponential model. ...

Activity-based models offer the potential of a far deeper understanding of daily mobility behaviour than trip-based models. However, activity-based models used both in research and practice have often relied on applying sequential choice models between sub ...

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We present a framework for performing regression when both covariate and response are probability distributions on a compact and convex subset of

\R^d

. Our regression model is based on the theory of optimal transport and links the conditional Fr'echet m ...

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In this thesis we present three closed form approximation methods for portfolio valuation and risk management. The first chapter is titled ``Kernel methods for portfolio valuation and risk management'', and is a joint work with Damir Filipovi'c (SFI and ...

For people with limited mobility, navigating in cluttered indoor environment is challenging. In this work, we propose a mobile assistive furniture suite that is designed to ease the life of people with special needs in indoor movement. To enable intelligen ...

Bayesian estimation of mixed multinomial logit models: Advances and simulation-based evaluations

Chattez avec Graph Search

On distributional autoregression and iterated transportation

Timing and spatial selection bias in rapid extreme event attribution

Positive Definite Completions and Continuous Graphical Models

Boosting likelihood learning with event reweighting

Stochastic pairwise preference convergence in Bayesian agents

Density estimation in RKHS with application to Korobov spaces in high dimensions

Regularization for distributionally robust state estimation and prediction

Deciphering the nature of cell state transitions in single cells using quantitative modeling of temporal dynamics

A Hardware-Efficient EMG Decoder with an Attractor-based Neural Network for Next-Generation Hand Prostheses

Higher Order Asymptotics: Applications to Satellite Conjunction and Boundary Problems

OASIS: Optimisation-based Activity Scheduling with Integrated Simultaneous choice dimensions

Graph generative deep learning models with an application to circuit topologies

Time vs. Truth: Age-Distortion Tradeoffs and Strategies for Distributed Inference

Random matrix methods for high-dimensional machine learning models

Distributional Regression and Autoregression via Optimal Transport

Data-driven modeling of beam loss in the LHC

Closed form approximation methods for portfolio valuation and risk management

Real-Time Localization for Closed-Loop Control of Assistive Furniture