Data for "Prediction error, prior certainty, or belief updating: P3a component function in temporal Bayesian inference
In a Bayesian analysis the statistician must specify prior densities for the model parameters. If he...
We introduce a new sequential algorithm for making robust predictions in the presence of changepoint...
International audienceAbstractWe provide some objective foundations for a belief revision process in...
Probabilities after hypothesis exploration using Bayesian multimodel inference.</p
1. Data analysis via Bayes ’ Rule, telling us how to update priors beliefs in light o
We propose a framework for general Bayesian inference. We argue that a valid update of a prior belie...
Many cognitive processes, ranging from perception to action, depend on the ability to predict the ti...
In Bayesian model updating, probability density functions of model parameters are updated accounting...
International audienceWe study a new approach to statistical prediction in the Dempster-Shafer frame...
Performance of the best three inference models with and without prior knowledge.</p
Bayesian inference under imprecise prior information is studied: the starting point is a precise str...
<p><b>A.</b> The parameters that define the logistic function are illustrated: λ<sub>A</sub>, lapse ...
There is a nearly ubiquitous assumption in PSA that parameter values are at least piecewise-constant...
In Bayesian model updating, probability density functions of model parameters are updated accounting...
This work examines important issues in probabilistic temporal representation and reasoning using Bay...
In a Bayesian analysis the statistician must specify prior densities for the model parameters. If he...
We introduce a new sequential algorithm for making robust predictions in the presence of changepoint...
International audienceAbstractWe provide some objective foundations for a belief revision process in...
Probabilities after hypothesis exploration using Bayesian multimodel inference.</p
1. Data analysis via Bayes ’ Rule, telling us how to update priors beliefs in light o
We propose a framework for general Bayesian inference. We argue that a valid update of a prior belie...
Many cognitive processes, ranging from perception to action, depend on the ability to predict the ti...
In Bayesian model updating, probability density functions of model parameters are updated accounting...
International audienceWe study a new approach to statistical prediction in the Dempster-Shafer frame...
Performance of the best three inference models with and without prior knowledge.</p
Bayesian inference under imprecise prior information is studied: the starting point is a precise str...
<p><b>A.</b> The parameters that define the logistic function are illustrated: λ<sub>A</sub>, lapse ...
There is a nearly ubiquitous assumption in PSA that parameter values are at least piecewise-constant...
In Bayesian model updating, probability density functions of model parameters are updated accounting...
This work examines important issues in probabilistic temporal representation and reasoning using Bay...
In a Bayesian analysis the statistician must specify prior densities for the model parameters. If he...
We introduce a new sequential algorithm for making robust predictions in the presence of changepoint...
International audienceAbstractWe provide some objective foundations for a belief revision process in...
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