搜索结果: 1-6 共查到“理论统计学 Inferring”相关记录6条 . 查询时间(0.026 秒)
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 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 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 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...