Assessing model credibility under uncertainty using Bayesian
Bayesian Model Averaging and Endogeneity Under Model Uncertainty: An Application to Development Determinants
... substantial model uncertainty at both the instrument and the development determinant ...level. Bayesian Model Av- eraging (BMA) has been proven useful in resolving model ...
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A Bayesian Model of Knightian Uncertainty
... modern Bayesian language, agents care only about the prizes they receive, not whether they were the result of risk rather than ...the uncertainty premium. This section also introduces a very simple ...
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Optimal Policy Under Model Uncertainty: A Structural-Bayesian Estimation Approach
... policy under model uncertainty In this section we describe the general framework and our novel methodology to analyze the opti- mal conduct of policy if the decision maker faces model ...
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Bayesian Optimization for Learning Gaits under Uncertainty
... evaluate Bayesian optimization, a model-based approach to black- box optimization under uncertainty, on both simulated problems and real ...
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Uncertainty Analysis of a Temperature-Index Snowmelt Model Using Bayesian Networks
... DM model and the BN model discretized at D20 and ...DM model and BN versions, exhibit similar time evolution ...BN model is a graphical implementation of deterministic equations making up ...
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INFORMATION SECURITY RISK ASSESSMENT UNDER UNCERTAINTY USING DYNAMIC BAYESIAN NETWORKS
... the uncertainty in the risk events and the additional tedious task of decision making under risk makes the risk management process ...Dynamic Bayesian Network models are constructed to identify multi ...
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Model Uncertainty and Bayesian Model Averaging in Vector Autoregressive Processes
... single model, when several viable models exist, limits its ...of model uncertainty, a Bayesian model averaging procedure is presented which allows for unconditional inference within the ...
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Applying Bayesian networks to model uncertainty in project scheduling
... In reality most uncertain events of interest do not have a lot of historical data associated with them and even where relevant historical data does exist it must still usually be informed by subjective judgements before ...
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A Formal, Bayesian Approach for Uncertainty Analysis of a Watershed Model
... and model predictive uncertainty essentially relies on the formulation of the likelihood function used to summarize the mismatch between model predictions and ...between model simulations and ...
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Valuing Inputs Under Supply Uncertainty : The Bayesian Shapley Value
... the Bayesian Shapley ...rationalizing uncertainty when the inputs are rational workers supplying labor in a non-cooperative production game in which payoffs are given by the Shapley wage ...condition ...
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Valuing Inputs Under Supply Uncertainty : The Bayesian Shapley Value
... the Bayesian Shapley ...rationalizing uncertainty when the inputs are rational workers supplying labor in a non-cooperative production game in which payoffs are given by the Shapley wage ...condition ...
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Management interventions in dairy herds: exploring within herd uncertainty using an integrated Bayesian model
... the uncertainty in outcome associated with undertaking a specific control strategy has rarely been considered in veterinary ...the uncertainty in change in disease incidence and financial benefit that could ...
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Decision-making under uncertainty – the integrated approach of the AHP and Bayesian analysis
... decisions under uncertainty can be performed only with the help of additional informa- tion, in order to reduce the impact of ...uncertainty. Bayesian analysis updates information using ...
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Genealogical working distributions for Bayesian model testing with phylogenetic uncertainty
... demographic model that is specified as a tree prior in a Bayesian genealogical ...iteration. Using simulated Gaussian data, for which we can analytically calculate the true marginal likelihood, we ...
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Impact of petrophysical uncertainty on Bayesian hydrogeophysical inversion and model selection
... scatter). Using synthetic examples and crosshole ground-penetrating radar (GPR) data from the South Oyster Bacterial Transport Site in Virginia, USA, we investigate the impact of spatially-correlated petrophysical ...
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Quantifying uncertainty in brain network measures using Bayesian connectomics
... Thus, uncertainty is propagated to the level of graph-theoretical ...of Bayesian connectomics, we use diffusion imaging data collected for twenty subjects (these are shown in Figure S2 as streamline count ...
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Bayesian Optimisation for Planning under Uncertainty
... Although using finite dimensional approximations for feature maps in the policies representation, the dimensionality of the search space can be still quite ...A Bayesian optimisation method to to solve ...
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Inverse uncertainty quantification of input model parameters for thermal-hydraulics simulations using expectation-maximization under non-Bayesian and Bayesian framework
... TRACE model for FEBA reflooding experiment described in Chapter 3 and calculate the sensitivities of the TRACE code parameters (physical models), hence creating a ranking of the most important ...TRACE ...
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Green Investment under Policy Uncertainty and Bayesian Learning
... the model, the investor faces both policy uncertainty and uncertain electricity ...policy uncertainty may introduce risk in the environment given by fixed FIT regimes, due to the likelihood of a ...
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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 ...of uncertainty, this thesis focuses on quantifying the model discrepancy (physical model ...
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