搜索结果: 16-30 共查到“知识库 统计学其他学科”相关记录659条 . 查询时间(4.439 秒)
The student's dilemma: ranking and improving prediction at test time without access to training data
The student's dilemma ranking improving prediction test time without access training data
2013/4/27
The standard approach to rank the performance of several classifiers for a given classification problem is via an independent labeled validation dataset. However, in various applications only unlabele...
Risk Prediction of a Multiple Sclerosis Diagnosis
Risk Prediction a Multiple Sclerosis Diagnosis
2013/4/27
Multiple sclerosis (MS) is a chronic autoimmune disease that affects the central nervous system. The progression and severity of MS varies by individual, but it is generally a disabling disease. Altho...
Does the specification of uncertainty hurt the progress of scientometrics?
specification of uncertainty progress of scientometrics
2012/11/23
In "Caveats for using statistical significance tests in research assessments,"--Journal of Informetrics 7(1)(2013) 50-62, available at arXiv:1112.2516 -- Schneider (2013) focuses on Opthof & Leydesdor...
Experimental design for Partially Observed Markov Decision Processes
Experimental design Partially Observed Markov Decision Processes
2012/11/22
This paper deals with the question of how to most effectively conduct experiments in Partially Observed Markov Decision Processes so as to provide data that is most informative about a parameter of in...
Cramer-Rao-Induced Bounds for CANDECOMP/PARAFAC tensor decomposition
CANDECOMP/PARAFAC Cramer-Rao-Induced tensor decomposition Bounds
2012/11/22
This paper presents a Cramer-Rao lower bound (CRLB) on the variance of unbiased estimates of factor matrices in Canonical Polyadic (CP) or CANDECOMP/PARAFAC (CP) decompositions of a tensor from noisy ...
Generating Markov Equivalent Maximal Ancestral Graphs by Single Edge Replacement
Markov Equivalent Maximal Ancestral Single Edge Replacement
2012/9/19
Maximal ancestral graphs(MAGs) are used to encode conditional independence relations in DAG models with hidden variables. Di erent MAGs may represent the same set of con-ditional independences and are...
Toward Practical N2 Monte Carlo: the Marginal Particle Filter
Practical N2 Monte Carlo Marginal Particle Filter
2012/9/19
Sequential Monte Carlo techniques are useful for state estimation in non-linear, non-Gaussian dy-namic models. These methods allow us to ap-proximate the joint posterior distribution using sequential ...
The Graphical Identification for Total Effects by using Surrogate Variables
Graphical Identification Total Effects Surrogate Variables
2012/9/19
Consider the case where cause-effect relation-ships between variables can be described as a directed acyclic graph and the corresponding linear structural equation model. This paper provides graphical...
Dependent Dirichlet Priors and Optimal Linear Estimators for Belief Net Parameters
Dependent Dirichlet Priors Optimal Linear Estimators Belief Net Parameters
2012/9/19
A Bayesian belief network is a model of a joint distribution over a finite set of vari-ables, with a DAG structure representing im-mediate dependencies among the variables.For each node, a table of pa...
ARMA Time-Series Modeling with Graphical Models
ARMA Time-Series Modeling Graphical Models
2012/9/19
We express the classic ARMA time-series model as a directed graphical model. In doing so, we find that the deterministic re-lationships in the model make it effectively impossible to use the EM algori...
Convergence and asymptotic normality of variational Bayesian approximations for exponential family models with missing values
Convergence asymptotic normality variational Bayesian approximations exponential family models missing values
2012/9/19
We study the properties of variational Bayes approximations for exponential family mod-els with missing values. It is shown that the iterative algorithm for obtaining the varia-tional Bayesian estimat...
From Fields to Trees
From Fields Trees
2012/9/19
We present new MCMC algo rithms for com-puting the posterior distri but ionsandexpec-tations of the unknownvari ables in undi-rected grap hical model swi thre gular struc ture. For demon stration pu r...
Modeling Waveform Shapes with Random Eects Segmental Hidden Markov Models
Modeling Waveform Shapes Random Eects Segmental Hidden Markov Models
2012/9/19
In this paper we describe a general probabilis-tic framework for modeling waveforms such as heartbeats from ECGdata. The model is based on segmental hidden Markov mod-els(as usedin speechrecognition) ...
Modeling Waveform Shapes with Random Eects Segmental Hidden Markov Models
Modeling Waveform Shapes Random Eects Segmental Hidden Markov Models
2012/9/19
In this paper we describe a general probabilis-tic framework for modeling waveforms such as heartbeats from ECGdata. The model is based on segmental hidden Markov mod-els(as usedin speechrecognition) ...
Selection of Identifiability Criteria for Total Effects by using Path Diagrams
Selection Identifiability Criteria Total Effects Path Diagrams
2012/9/19
Pearl has provided the back door criterion,the front door criterion and the conditional instrumental variable (IV) method as iden-tifiability criteria for total effects.In some situations,these three ...