Results 41 to 50 of about 1,818,807 (197)
An algorithm to estimate parameters and states of a nonlinear maneuvering target.
Cogent Engineering, 2020 This paper investigates the problem of unknown input estimation such as acceleration, target class, and maneuvering target tracking using a hybrid algorithm. One of the challenges of unknown input estimation is that no effective method has been presented
S.N. Hosseini, M. Haeri, H. Khaloozadeh
doaj +1 more source
To vary or not to vary: A flexible empirical Bayes factor for testing variance components
British Journal of Mathematical and Statistical Psychology, EarlyView.Abstract Random effects are the gold standard for capturing structural heterogeneity, such as individual differences or temporal dependence. Yet testing their presence is difficult because variance components are constrained to be non‐negative, creating a boundary problem. This paper introduces a flexible empirical Bayes factor (EBF) for testing random
Fabio Vieira, Hongwei Zhao, Joris Mulder
wiley +1 more source
Prior distributions for variance parameters in hierarchical models [PDF]
Various noninformative prior distributions have been suggested for scale parameters in hierarchical models. We construct a new folded-noncentral- t family of conditionally conjugate priors for hierarchical standard deviation parameters, and then consider
Andrew Gelman
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Default Priors in a Zero-Inflated Poisson Distribution: Intrinsic Versus Integral Priors
MathematicsPrior elicitation is an important issue in both subjective and objective Bayesian frameworks, where prior distributions impose certain information on parameters before data are observed.
Junhyeok Hong, Kipum Kim, Seong W. Kim
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On Integral Priors for Multiple Comparison in Bayesian Model Selection
International Statistical Review, EarlyView.Summary Noninformative priors constructed for estimation purposes are usually not appropriate for model selection and testing. The methodology of integral priors was developed to get prior distributions for Bayesian model selection when comparing two models, modifying initial improper reference priors. We propose a generalisation of this methodology to
Diego Salmerón +2 more
wiley +1 more source
Advances in Civil Engineering, 2019 In order to improve the estimation accuracy of bearing capacity of pile foundation, a new forecast method of bearing capacity of pile foundation was proposed on Jeffrey’s noninformative prior using the MCMC (Markov chain Monte Carlo) method of the ...
Zuolong Luo, Fenghui Dong
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The Development of Subitizing in Bilingual Children
Developmental Science, Volume 30, Issue 1, January 2027.ABSTRACT What role does language‐specific experience play in the development of numerical knowledge? Previous studies have probed this question by testing bilingual learners, who have different linguistic representations of number across their two languages.
Elisabeth Marchand +4 more
wiley +1 more source
Paired Comparison Analysis of the van Baaren Model Using Bayesian Approach with Noninformative Prior [PDF]
, 2012 One technique being commonly studied these days because of its attractive applications for the comparison of several objects is the method of paired comparisons.
Altaf, Saima, Aslam, Muhammad
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Jeffreys noninformative prior in bayesian conjoint analysis
, 1999 컨조인트 분석은 어떤 제품이 갖고 있는 속성 하나하나에 고객이 부여하는 효용을 추정함으로써 그 고객이 어떤 제품을 선택할지를 예측하는 통계적 기법이다. 컨조인트 분석의 목표는 고객 개개인이 개별 속성의 각 수준에 대해 얼마만큼의 선호도를 부여하는지를 추정하는 데 있다. 컨조인트 모델을 분석하기 위해 무정보 사전분포인 Jeffreys 사전분포를 이용한 베이지안 분석을 적용해 보았다.
김유라
core +1 more source
The Impact of Prior Information on Bayesian Latent Basis Growth Model Estimation
SAGE Open, 2017 Latent basis growth modeling is a flexible version of the growth curve modeling, in which it allows the basis coefficients of the model to be freely estimated, and thus the optimal growth trajectories can be determined from the observed data.
Dingjing Shi, Xin Tong
doaj +1 more source