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Inferring Stable Genetic Networks from Steady-State Data
convex optimization stability of linear systems
2015/7/9
Gene regulatory networks capture the interactions between genes and other cell substances, resulting from the fundamental biological process of transcription and translation. In some applications, the...
Inferring Team Strengths Using a Discrete Markov Random Field
Inferring Team Strengths Discrete Markov Random Field
2013/6/14
We propose an original model for inferring team strengths using a Markov Random Field, which can be used to generate historical estimates of the offensive and defensive strengths of a team over time. ...
Inferring ground truth from multi-annotator ordinal data: a probabilistic approach
ground truth multi-annotator ordinal data a probabilistic approach
2013/6/13
A popular approach for large scale data annotation tasks is crowdsourcing, wherein each data point is labeled by multiple noisy annotators. We consider the problem of inferring ground truth from noisy...
Inferring land use from mobile phone activity
Urban Computing Human Mobility Land Use Dynamic Population Mobile Phone Data Computational Social Sci-ence
2012/9/18
Understanding the spatiotemporal distribution of people within a city is crucial to many planning applications. Obtaining data to create required knowledge, currently involves costly survey methods. A...
Inferring Climate System Properties Using a Computer Model
model calibration climate change climate sensitivity Bayesian methods
2009/9/22
A method is presented to estimate the probability distributions of
climate system properties based on a hierarchical Bayesian model. At the base
of the model, we use simulations of a climate model i...
Inferring Particle Distribution in a Proton Accelerator Experiment
computer simulator inverse problem exponentially-dampened cosine correlation
2009/9/21
A beam of protons is produced by a linear charged particle accelerator,
then focused through the use of successive quadrupoles. The initial state of the
beamis unknown, interms of particle position ...
Inferring sparse Gaussian graphical models with latent structure
Gaussian graphical model Mixture model ℓ 1-penalization Model selection Variational inference EM algorithm
2009/9/16
Our concern is selecting the concentration matrix's nonzero coefficients for a sparse Gaussian graphical model in a high-dimensional setting. This corresponds to estimating the graph of conditional de...
Inferring dynamic genetic networks with low order independencies
conditional independence Dynamic Bayesian Network DirectedAcyclic Graph networks inference time series modelling
2010/4/28
In this paper, we propose a novel inference method for dynamic genetic networks
which makes it possible to deal with a number of time measurements
n much smaller than the number of genes p. The appr...
INFERRING GENE DEPENDENCY NETWORKS FROM GENOMIC LONGITUDINAL DATA：A FUNCTIONAL DATA APPROACH
graphical model longitudinal data dynamical correlation gene dependency networks
2009/2/25
A key aim of systems biology is to unravel the regulatory interactions among genes
and gene products in a cell. Here we investigate a graphical model that treats the
observed gene expression over ti...
Inferring Markov Chains：Bayesian Estimation，Model Comparison，Entropy Rate，and Out-of-class Modeling
Markov Chains Bayesian Estimation Model Comparison Entropy Rate Out-of-class Modeling
2010/4/27
Markov chains are a natural and well understood tool for describing one-dimensional patterns in
time or space. We show how to infer k-th order Markov chains, for arbitrary k, from finite data
by app...