The Experts below are selected from a list of 79956 Experts worldwide ranked by ideXlab platform
Nelson Kinnersley - One of the best experts on this subject based on the ideXlab platform.
-
bayesian fitting of a logistic dose response curve with numerically derived Priors
Pharmaceutical Statistics, 2008Co-Authors: Les Huson, Nelson KinnersleyAbstract:In this report we describe the Bayesian analysis of a logistic dose-response curve in a Phase I study, and we present two simple and intuitive numerical approaches to construction of Prior Probability distributions for the model parameters. We combine these Priors with the expert Prior opinion and compare the results of the analyses with those obtained from the use of alternative Prior formulations.
-
bayesian fitting of a logistic dose response curve with numerically derived Priors
Pharmaceutical Statistics, 2008Co-Authors: Les Huson, Nelson KinnersleyAbstract:In this report we describe the Bayesian analysis of a logistic dose–response curve in a Phase I study, and we present two simple and intuitive numerical approaches to construction of Prior Probability distributions for the model parameters. We combine these Priors with the expert Prior opinion and compare the results of the analyses with those obtained from the use of alternative Prior formulations. Copyright © 2008 John Wiley & Sons, Ltd.
Les Huson - One of the best experts on this subject based on the ideXlab platform.
-
bayesian fitting of a logistic dose response curve with numerically derived Priors
Pharmaceutical Statistics, 2008Co-Authors: Les Huson, Nelson KinnersleyAbstract:In this report we describe the Bayesian analysis of a logistic dose-response curve in a Phase I study, and we present two simple and intuitive numerical approaches to construction of Prior Probability distributions for the model parameters. We combine these Priors with the expert Prior opinion and compare the results of the analyses with those obtained from the use of alternative Prior formulations.
-
bayesian fitting of a logistic dose response curve with numerically derived Priors
Pharmaceutical Statistics, 2008Co-Authors: Les Huson, Nelson KinnersleyAbstract:In this report we describe the Bayesian analysis of a logistic dose–response curve in a Phase I study, and we present two simple and intuitive numerical approaches to construction of Prior Probability distributions for the model parameters. We combine these Priors with the expert Prior opinion and compare the results of the analyses with those obtained from the use of alternative Prior formulations. Copyright © 2008 John Wiley & Sons, Ltd.
Michael S Landy - One of the best experts on this subject based on the ideXlab platform.
-
human online adaptation to changes in Prior Probability
PLOS Computational Biology, 2019Co-Authors: Elyse Norton, Luigi Acerbi, Michael S LandyAbstract:Optimal sensory decision-making requires the combination of uncertain sensory signals with Prior expectations. The effect of Prior Probability is often described as a shift in the decision criterion. Can observers track sudden changes in Probability? To answer this question, we used a change-point detection paradigm that is frequently used to examine behavior in changing environments. In a pair of orientation-categorization tasks, we investigated the effects of changing probabilities on decision-making. In both tasks, category Probability was updated using a sample-and-hold procedure: Probability was held constant for a period of time before jumping to another Probability state that was randomly selected from a predetermined set of Probability states. We developed an ideal Bayesian change-point detection model in which the observer marginalizes over both the current run length (i.e., time since last change) and the current category Probability. We compared this model to various alternative models that correspond to different strategies-from approximately Bayesian to simple heuristics-that the observers may have adopted to update their beliefs about probabilities. While a number of models provided decent fits to the data, model comparison favored a model in which Probability is estimated following an exponential averaging model with a bias towards equal Priors, consistent with a conservative bias, and a flexible variant of the Bayesian change-point detection model with incorrect beliefs. We interpret the former as a simpler, more biologically plausible explanation suggesting that the mechanism underlying change of decision criterion is a combination of on-line estimation of Prior Probability and a stable, long-term equal-Probability Prior, thus operating at two very different timescales.
-
human online adaptation to changes in Prior Probability
bioRxiv, 2018Co-Authors: Elyse Norton, Luigi Acerbi, Michael S LandyAbstract:Optimal sensory decision-making requires the combination of uncertain sensory signals with Prior expectations. The effect of Prior Probability is often described as a shift in the decision criterion. Can observers track sudden changes in Probability? To answer this question, we used a change-point detection paradigm that is frequently used to examine behavior in changing environments. In a pair of orientation-categorization tasks, we investigated the effects of changing probabilities on decision-making. In both tasks, category Probability was updated using a sample-and-hold procedure. We developed an ideal Bayesian change-point detection model in which the observer marginalizes over both the current run length (i.e., time since last change) and the current category Probability. We compared this model to various alternative models that correspond to different strategies -- from approximately Bayesian to simple heuristics -- that the observers may have adopted to update their beliefs about probabilities. We find that Probability is estimated following an exponential averaging model with a bias towards equal Priors, consistent with a conservative bias. The mechanism underlying change of decision criterion is a combination of on-line estimation of Prior Probability and a stable, long-term equal-Probability Prior, thus operating at two very different timescales.
Srdan Budimir - One of the best experts on this subject based on the ideXlab platform.
-
Prior Probability distributions of neutron star crust models
The Astrophysical Journal, 2021Co-Authors: Lauren Balliet, William G Newton, S A Cantu, Srdan BudimirAbstract:To make best use of multi-faceted astronomical and nuclear data-sets, Probability distributions of neutron star models that can be used to propagate errors consistently from one domain to another are required. We take steps toward a consistent model for this purpose, highlight where model inconsistencies occur and assess the resulting model uncertainty. Using two distributions of nuclear symmetry energy parameters - one uniform, the other based on pure neutron matter theory, we prepare two ensembles of neutron star inner crust models. We use an extended Skyrme energy-density functional within a compressible liquid drop model (CLDM). We fit the surface parameters of the CLDM to quantum 3D Hartree-Fock calculations of crustal nuclei. All models predict more than 50% of the crust by mass and 15% of the crust by thickness comprises pasta with medians of around 62% and 30% respectively. We also present 68% and 95% ranges for the crust composition as a function of density. We examine the relationships between crust-core boundary and pasta transition properties, the thickness of the pasta layers, the symmetry energy at saturation and sub-saturation densities and the neutron skins of 208Pb and 48Ca. We quantify the correlations using the maximal information coefficient, which can effectively characterize non-linear relationships. Future measurements of neutron skins, information from nuclear masses and giant resonances, and theoretical constraints on PNM will be able to place constraints on the location of the pasta and crust-core boundaries and the amount of pasta in the crust.
Henrik Singmann - One of the best experts on this subject based on the ideXlab platform.
-
a new probabilistic explanation of the modus ponens modus tollens asymmetry
Cognitive Science, 2019Co-Authors: Benjamin Eva, Stephan Hartmann, Henrik SingmannAbstract:A consistent finding in research on conditional reasoning is that individuals are more likely to endorse the valid modus ponens (MP) inference than the equally valid modus tollens (MT) inference. This pattern holds for both abstract task and probabilistic task. The existing explanation for this phenomenon within a Bayesian framework (e.g., Oaksford & Chater, 2008) accounts for this asymmetry by assuming separate Probability distributions for both MP and MT. We propose a novel explanation within a computational-level Bayesian account of reasoning according to which “argumentation is learning”. We show that the asymmetry must appear for certain Prior Probability distributions, under the assumption that the conditional inference provides the agent with new information that is integrated into the existing knowledge by minimizing the Kullback-Leibler divergence between the posterior and Prior Probability distribution. We also show under which conditions we would expect the opposite pattern, an MT-MP asymmetry.
