Maria Skoularidou
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View article: A Refined Vestibular Romberg Test to Differentiate Somatosensory from Vestibular-Induced Disequilibrium
A Refined Vestibular Romberg Test to Differentiate Somatosensory from Vestibular-Induced Disequilibrium Open
Background: The vestibular Romberg test, which assesses the deterioration of balance while standing on rubber foam with closed eyes, is a well-established method in the physical neurological assessment of patients with peripheral vestibulo…
View article: Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI Open
In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude o…
View article: Bayesian Context Trees: Modelling and Exact Inference for Discrete Time Series
Bayesian Context Trees: Modelling and Exact Inference for Discrete Time Series Open
We develop a new Bayesian modelling framework for the class of higher-order, variable-memory Markov chains, and introduce an associated collection of methodological tools for exact inference with discrete time series. We show that a versio…
View article: Revisiting Context-Tree Weighting for Bayesian Inference
Revisiting Context-Tree Weighting for Bayesian Inference Open
We revisit the statistical foundation of the celebrated context tree weighting (CTW) algorithm, and we develop a Bayesian modelling framework for the class of higher-order, variable-memory Markov chains, along with an associated collection…
View article: Bayesian Context Trees: Modelling and exact inference for discrete time series
Bayesian Context Trees: Modelling and exact inference for discrete time series Open
We develop a new Bayesian modelling framework for the class of higher-order, variable-memory Markov chains, and introduce an associated collection of methodological tools for exact inference with discrete time series. We show that a versio…
View article: Modeling Tabular data using Conditional GAN
Modeling Tabular data using Conditional GAN Open
Modeling the probability distribution of rows in tabular data and generating realistic synthetic data is a non-trivial task. Tabular data usually contains a mix of discrete and continuous columns. Continuous columns may have multiple modes…
View article: Deep Tree Models for ‘Big’ Biological Data
Deep Tree Models for ‘Big’ Biological Data Open
The identification of useful temporal dependence structure in discrete time series data is an important component of algorithms applied to many tasks in statistical inference and machine learning, and used in a wide variety of problems acr…
View article: Estimating the Directed Information and Testing for Causality
Estimating the Directed Information and Testing for Causality Open
The problem of estimating the directed information rate between two discrete processes $\{X_n\}$ and $\{Y_n\}$ via the plug-in (or maximum-likelihood) estimator is considered. When the joint process $\{(X_n,Y_n)\}$ is a Markov chain of a g…