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Hierarchical Bayesian Modelling of Interoceptive Psychophysics
Courtin AS +4 more
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Hierarchical Bayesian models of social inference for probing persecutory delusional ideation.
Journal of Abnormal Psychology, 2020While persecutory delusions (PDs) have been linked to fallacies of reasoning and social inference, computational characterizations of delusional tendencies are rare. Here, we examined 151 individuals from the general population on opposite ends of the PD
A. Diaconescu +4 more
semanticscholar +1 more source
Accident Analysis and Prevention, 2018
Traffic crashes are more likely to occur at intersections where the traffic environment is complicated. In this study, a hybrid approach combining cluster analysis and hierarchical Bayesian models is developed to examine driver injury severity patterns ...
Zhenning Li +6 more
semanticscholar +1 more source
Traffic crashes are more likely to occur at intersections where the traffic environment is complicated. In this study, a hybrid approach combining cluster analysis and hierarchical Bayesian models is developed to examine driver injury severity patterns ...
Zhenning Li +6 more
semanticscholar +1 more source
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
This paper aims at proposing an unsupervised hierarchical nonparametric Bayesian framework for modeling axial data (i.e., observations are axes of direction) that can be partitioned into multiple groups, where each observation within a group is sampled ...
Wentao Fan, Lin Yang, N. Bouguila
semanticscholar +1 more source
This paper aims at proposing an unsupervised hierarchical nonparametric Bayesian framework for modeling axial data (i.e., observations are axes of direction) that can be partitioned into multiple groups, where each observation within a group is sampled ...
Wentao Fan, Lin Yang, N. Bouguila
semanticscholar +1 more source
2000
Publisher Summary This chapter describes the Bayesian hierarchical models. The evaluation of this study, by a Bayesian hierarchical linear model is derived from the data that include the other large clinical trials of thrombolytic therapy and suggests that treatment is also beneficial for patients, arriving much later than six hours after symptom ...
C H, Schmid, E N, Brown
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Publisher Summary This chapter describes the Bayesian hierarchical models. The evaluation of this study, by a Bayesian hierarchical linear model is derived from the data that include the other large clinical trials of thrombolytic therapy and suggests that treatment is also beneficial for patients, arriving much later than six hours after symptom ...
C H, Schmid, E N, Brown
openaire +2 more sources
Bayesian nonparametric hierarchical modeling
Biometrical Journal, 2009AbstractIn biomedical research, hierarchical models are very widely used to accommodate dependence in multivariate and longitudinal data and for borrowing of information across data from different sources. A primary concern in hierarchical modeling is sensitivity to parametric assumptions, such as linearity and normality of the random effects ...
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2015
This chapter seeks to explain hierarchical models and how they differ from simple Bayesian models and to illustrate building hierarchical models using mathematically correct expressions. It begins with the definition of hierarchical models. Next, the chapter introduces four general classes of hierarchical models that have broad application in ecology ...
N. Thompson Hobbs, Mevin B. Hooten
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This chapter seeks to explain hierarchical models and how they differ from simple Bayesian models and to illustrate building hierarchical models using mathematically correct expressions. It begins with the definition of hierarchical models. Next, the chapter introduces four general classes of hierarchical models that have broad application in ecology ...
N. Thompson Hobbs, Mevin B. Hooten
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Criterion constrained Bayesian hierarchical models
TEST, 2022zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Qingying Zong, Jonathan R. Bradley
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Ensuring identifiability in hierarchical mixed effects Bayesian models.
Ecological Applications, 2020Ecologists are increasingly familiar with Bayesian statistical modeling and its associated Markov chain Monte Carlo (MCMC) methodology to infer about or to discover interesting effects in data.
K. Ogle, Jarrett J. Barber
semanticscholar +1 more source