Projecting the cohort parameters using a Bayesian
Projecting UK mortality by using Bayesian generalized additive models
... the Bayesian approach that was developed above, in that the gap between the high and low variants is much narrower than the fan intervals for at least the first decade of the ...
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Bayesian age-period-cohort modeling and prediction - BAMP
... diagram, using a Bayesian version of an age-period-cohort ...and cohort parameters random walks of first and second order, with and without an additional unstructured component are ...
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Bayesian Age-Period-Cohort Modeling and Prediction - BAMP
... diagram, using a Bayesian version of an age-period-cohort ...and cohort parameters random walks of first and second order, with and without an additional unstructured component are ...
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Modelling and projecting mortality improvement rates using a cohort perspective
... x parameters, which coincides with the positioning of the characteristic „accident hump‟ that is associated with static period life tables; the more pronounced nature of the „accident hump‟ spike for males ...
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Modelling and projecting mortality improvement rates using a cohort perspective
... parameters, which coincides with x the positioning of the characteristic „accident hump‟ that is associated with static period life tables; the more pronounced nature of the „accident hump‟ spike for males ...
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Projecting Parameters for Multilingual Word Sense Disambiguation
... The above situation brings out the challenges involved in Indian language MT and CLIR. Lack of resources coupled with the multiplicity of Indian languages severely affects the performance of sev- eral NLP tasks. In the ...
9
Bayesian inference of biochemical kinetic parameters using the linear noise approximation
... distribution using the standard MH ...set using 20 sampled trajec- tories (see Figure ...specifications, parameters used for the simulations and inference results are pre- sented in Table ...all ...
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Are Bayesian Networks Sensitive to Precision of Their Parameters?
... that Bayesian network models are overall quite tolerant to imprecision in their numerical param- ...original parameters (assumed to be the gold standard) and measuring the influence of the magnitude of this ...
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Inverse uncertainty quantification of trace physical model parameters using Bayesian analysis
... systematic uncertainty, which is due to the fact that there are things that we do not know. Despite a variety of sources of uncertainty, this thesis focuses on quantifying the model discrepancy (physical model ...
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Emulation of reionization simulations for Bayesian inference of astrophysics parameters using neural networks
... for Bayesian parameter inference and how to optimally extract information from incoming data is currently ...analysis using 21CMMC. We find good predictive capabilities of our network using training ...
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Projecting Thailand physician supplies between 2012 and 2030: application of cohort approaches
... ician cohort data collection undertaken, there is a need for regular, routine and integrated collection of physician data by cohort, as part of a strengthening of the whole human resources for health ...
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Description of cervical cancer mortality in Belgium using Bayesian age-period-cohort models
... model parameters with the likelihood ...the Bayesian methods, much work has been carried out in developing simulation-based methods called Markov Chain Monte Carlo (MCMC) methods using Gibbs ...of ...
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Using Approximate Bayesian Computation to Estimate Tuberculosis Transmission Parameters From Genotype Data
... tational Bayesian methods may help to refine our un- derstanding of transmission patterns of ...of parameters while still retaining the ability to recover the most important of these in the estimation ...
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Towards Autonomous Reinforcement Learning: Automatic Setting of Hyper-parameters using Bayesian Optimization
... For future research work, there are several directions were this work is to be extended. The first direction points towards the extension of the performance functions and the respective analysis and comparison with the ...
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Using IRT parameters as informative priors in second-order Bayesian latent growth modeling
... Attempting to estimate individual-level autoregression terms in Equation 36, above, resulted in severe convergence problems, which were attenuated, but not eliminated, by using λ 3 instead of φ in the Kalman ...
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Inferring demographic parameters in bacterial genomic data using Bayesian and hybrid phylogenetic methods.
... We compared estimates of rates and evolutionary time- scales using the full Bayesian approach in BEAST2 and LSD. Because our data consist of SNPs, we used ascer- tainment bias correction by specifying the ...
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Bayesian estimation of parameters in a regional hydrological model
... area, using Bayesian statistics to estimate the posterior distribution of the model parameters conditioned on the observed ...The Bayesian method requires formulation of a likelihood function ...
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New Techniques for Learning Parameters in Bayesian Networks.
... In hybrid BNs, exact inference can only be performed when the network treewidth is small and the continuous nodes are assumed to be conditional Gaussian distributions (Lauritzen and Jensen, 2001) — a highly unrealistic ...
164
Inverse uncertainty quantification of input model parameters for thermal-hydraulics simulations using expectation-maximization under non-Bayesian and Bayesian framework
... its parameters is also quantified in a sensitivity ...code parameters (physical models), hence creating a ranking of the most important ...input parameters (TRACE physical models) are artificially ...
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Bayesian inference of genetic parameters for reproductive traits in Sistani native cows using Gibbs sampling
... Results The P-value obtained from Geweke algorithm sup- ported convergence for all chains. After convergence, 3000 and 20000 samples were used for the estimation of posterior means of genetic parameters in ...
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