[PDF] Top 20 Bayesian inference and model comparison for random choice structures
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Bayesian inference and model comparison for random choice structures
... probabilities for “Getting it right” computations ...Dana, and Davis-Stober’s (2011) experiment, 18 undergraduates participated in three different scenarios, denoted here and in that paper by “Cash ... Voir le document complet
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A Bayesian non-parametric hidden Markov random model for hemodynamic brain parcellation
... account for the HRF variability using more regressors and hence more flexible design matrices [24,23,29] ...Balloon model) have been pushed forward for recovering hemodynamics but most often ... Voir le document complet
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Bayesian inference of a parametric random ellipsoid from its orthogonal projections
... tribution and a truncated normal distribution for the semi-major axis and the flattening coefficient, ...error for most of the parameters in the case of the five statistics for both ... Voir le document complet
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A Bayesian non-parametric hidden Markov random model for hemodynamic brain parcellation
... estimation model addresses this issue by inferring the parcels from fMRI ...mixture model combined with a hidden Markov random field to estimate the parcels and their number ...strategy ... Voir le document complet
16
Computational Solutions for Bayesian Inference in Mixture Models
... the choice of the proposal ...A random walk is thus delicate to calibrate in such a context and it is often preferable to settle for a Gibbs sampler that updates one group of parameters at a ... Voir le document complet
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Bayesian Inference Underlies the Contraction Bias in Delayed Comparison Tasks
... psychology and neuroscience. Interestingly, choice behavior in these experiments reveals a fundamental bias: when the first stimulus is small, subjects tend to overestimate it, whereas when it is large, ... Voir le document complet
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Bayesian Inference for Periodic Regime-Switching Models
... models for seasonal ...frequency and its harmonics. The time series models considered can, for instance, predict that, say booms in housing starts are less likely to take o in the winter, that stock ... Voir le document complet
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A Bayesian Non-Parametric Hidden Markov Random Model for Hemodynamic Brain Parcellation
... CEA/NeuroSpin and INRIA Saclay, Parietal, France Abstract Deriving a meaningful functional brain parcellation is a very challenging is- sue in task-related fMRI ...estimation model addresses this issue by ... Voir le document complet
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Bayesian Modelling and Inference on Mixtures of Distributions
... RJMCMC and BDMCMC algorithms and their ...birth and death moves with birth proposals based on the prior distribution, enjoys similar properties to ...that for any BDMCMC process satisfying ... Voir le document complet
56
Lack of confidence in approximate Bayesian computation model choice
... Gibbs random fields can be extended to any exponential family (hence to any setting with fixed-dimension sufficient statistics; see ...tics and all dominating measure statistics in an encompassing ... Voir le document complet
7
Bayesian statistical inference and deep learning for primordial cosmology and cosmic acceleration
... local and global informa- tion to propagate through the network (Lecun, Bengio, and Hinton, 2015 ...useful for image processing and pattern recognition, and are therefore gaining in ... Voir le document complet
230
Attention as a Bayesian inference process
... 2. Comparison of the proposed Bayesian model with shape-based features with prior work that relies on low level ...44 and fixations in the most salient region (F M SR) from. 41, 45 For ... Voir le document complet
11
ABC random forests for Bayesian parameter inference
... reviewed and recommended by Peer Community In Evolutionary Biology ( http:// ...Approximate Bayesian computation (ABC) has grown into a standard methodology that manages Bayesian inference ... Voir le document complet
38
Bayesian inference for compact binary sources of gravitational waves
... apples-to-apples comparison with the current performances of ...algorithm for BNS parameter estimation using a TaylorF2 waveform ...waveforms and the log-likelihood are not as optimised as those ... Voir le document complet
195
Bayesian Inference for Mixtures of Stable Distributions
... mixture model in order to capture the heterogeneity of ...account for multimodality, which is present, for example, in financial ...tools for stable distributions ...(see for example ... Voir le document complet
50
Amount of information needed for model choice in Approximate Bayesian Computation
... Approximate Bayesian Computation (ABC) has become a popular technique in evolutionary genetics for elucidating population structure and history due to its ...statistical inference framework ... Voir le document complet
14
Bayesian numerical inference for hidden Markov models
... the Bayesian inference is mainly due to the development of efficient Monte Carlo approximation techniques ...techniques and should be approached with realistic ...numerical Bayesian ... Voir le document complet
7
Approximate Decentralized Bayesian Inference
... step; and hyperparameter consensus (Fraser et ...sends and receives statistics to and from other agents in the net- work asynchronously, and combines the posteriors locally ...distributed ... Voir le document complet
11
Bayesian Inference for Parametric Growth Incidence Curves
... statistical inference in the tails of the ...function and the Lorenz curve to propose a series of parametric forms for growth incidence curves within a Bayesian ...densities for which ... Voir le document complet
26
Bayesian inference algorithm on Raw
... For the medium grain implementation, the problem size is split among the tiles (not replicated), so the total load-up time should remain about the same as the number of [r] ... Voir le document complet
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