Concept

Conditional probability table

Related publications (22)

Distributionally Robust Optimization with Polynomial Densities: Theory, Models and Algorithms

In distributionally robust optimization the probability distribution of the uncertain problem parameters is itself uncertain, and a fictitious adversary, e.g., nature, chooses the worst distribution from within a known ambiguity set. A common shortcoming o ...

2020

On quantifying the quality of acoustic models in hybrid DNN-HMM ASR

Hervé Bourlard, Afsaneh Asaei, Pranay Dighe

We propose an information theoretic framework for quantitative assessment of acoustic models used in hidden Markov model (HMM) based automatic speech recognition (ASR). The HMM backend expects that (i) the acoustic model yields accurate state conditional e ...

ELSEVIER2020

Safely Managed Hygiene: A Risk-Based Assessment of Handwashing Water Quality

Matthew Eric Verbyla, Timothy R. Julian

Sustainable Development Goal (SDG) Indicator 6.2.1 requires household handwashing facilities to have soap and water, but there are no guidelines for handwashing water quality. In contrast, drinking water quality guidelines are defined: water must be "free ...

AMER CHEMICAL SOC2019

Multi-Modal Mean-Fields via Cardinality-Based Clamping

Pascal Fua, François Fleuret

Mean Field inference is central to statistical physics. It has attracted much interest in the Computer Vision community to efficiently solve problems expressible in terms of large Conditional Random Fields. However, since it models the posterior probabilit ...

Ieee2017

Multi-Modal Mean-Fields via Cardinality-Based Clamping

Pascal Fua, François Fleuret

Ieee2017

Learning and generation of slow sequences: an application to music composition

Florian François Colombo

Human brains can deal with sequences with temporal dependencies on a broad range of timescales, many of which are several order of magnitude longer than neuronal timescales. Here we introduce an artificial intelligence that learns and produces the complex ...

2016

High Resolution Air Pollution Maps in Urban Environments Using Mobile Sensor Networks

Alcherio Martinoli, Ali Marjovi, Adrian Arfire

We propose three modeling methods using a mobile sensor network to generate high spatio-temporal resolution air pollution maps for urban environments. In our deployment in Lausanne (Switzerland), dedicated sensing nodes are anchored to the public buses and ...

IEEE2015

Pattern-wise trust assessment of sensor data

Karl Aberer, Mehdi Riahi, Robert Gwadera

One of the most important tasks of a sensor network (SN) is to detect occurrences of interesting events in the monitored environment. However, data measured by SN is often affected by errors. We investigate the problem of assessing trustworthiness (trust) ...

2014

A bottom-up stochastic model to predict building occupants' time-dependent activities

Jean-Louis Scartezzini, Darren Robinson, Frédéric Haldi, Urs Wilke

A bottom-up modelling approach together with a set of calibration methodologies is presented to predict residential building occupants' time-dependent activities, for use in dynamic building simulations. The stochastic model to predict activity chains is c ...

Elsevier2013

Conditional probabilities in the excursion set theory: generic barriers and non-Gaussian initial conditions

Andrea De Simone, Michele Maggiore

The excursion set theory, where density perturbations evolve stochastically with the smoothing scale, provides a method for computing the dark matter halo mass function. The computation of the mass function is mapped into the so-called first-passage time p ...

Blackwell Publishing Ltd, Oxford2011

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