A kernel-based Bayesian approach to climatic reconstruction
- Projection of future climate change conditions using IPCC simulations, neural networks and Bayesian statistics. Part 1: Temperature mean state and seasonal cycle in South America. Boulanger, Jean-Philippe; Martinez, Fernando; Segura, Enrique C. // Climate Dynamics;Sep2006, Vol. 27 Issue 2/3, p233
Projections for South America of future climate change conditions in mean state and seasonal cycle for temperature during the twenty-first century are discussed. Our analysis includes one simulation of seven Atmospheric-Ocean Global Circulation Models, which participated in the Intergovernmental...
- Forecast Calibration and Combination: A Simple Bayesian Approach for ENSO. Coelho, C. A. S.; Pezzulli, S.; Balmaseda, M.; Doblas-Reyes, F. J.; Stephenson, D. B. // Journal of Climate;Apr2004, Vol. 17 Issue 7, p1504
This study presents a new simple approach for combining empirical with raw (i.e., not bias corrected) coupled model ensemble forecasts in order to make more skillful interval forecasts of ENSO. A Bayesian normal model has been used to combine empirical and raw coupled model December SST...
- Bayesian Approach to Decision Making Using Ensemble Weather Forecasts. Katz, Richard W.; Ehrendorfer, Martin // Weather & Forecasting;Apr2006, Vol. 21 Issue 2, p220
The economic value of ensemble-based weather or climate forecasts is generally assessed by taking the ensembles at ï¿½face value.ï¿½ That is, the forecast probability is estimated as the relative frequency of occurrence of an event among a limited number of ensemble members. Despite the...
- Catchment classification: empirical analysis of hydrologic similarity based on catchment function in the eastern USA. Sawicz, K.; Wagener, T.; Sivapalan, M.; Troch, P. A.; Carrillo, G. // Hydrology & Earth System Sciences;2011, Vol. 15 Issue 9, p2895
Hydrologic similarity between catchments, derived from similarity in how catchments respond to precipitation input, is the basis for catchment classification, for transferability of information, for generalization of our hydrologic understanding and also for understanding the potential impacts...
- Application of a Full Hierarchical Bayesian Model in Assessing Streamflow Response to a Climate Change Scenario at the Coweeta Basin, NC, USA. Wu Wei; Clark, James S.; Vose, James M. // Journal of Resources & Ecology;Jun2012, Vol. 3 Issue 2, p118
We have applied a full hierarchical Baysian (HB) model to simulate streamflow at the Coweeta Basin that drains western North Carolina, USA under a doubled CO2 climate scenario. The full HB model coherently assimilated multiple data sources and accounted for uncertainties from data, parameters...
- Bayesian objective classification of extreme UK daily rainfall for flood risk applications. Little, M. A.; Rodda, H. J. E.; McSharry, P. E. // Hydrology & Earth System Sciences Discussions;2008, Vol. 5 Issue 6, p3033
In this study we describe an objective classification scheme for extreme UK daily precipitation to be used in flood risk analysis applications. We create a simplified representation of the spatial layout of extreme events based on a new digital archive of UK rainfall. This simplification allows...
- Bayesian, Likelihood, and Frequentist Approaches to Statistics. Senn, Stephen // Applied Clinical Trials;Aug2003, Vol. 12 Issue 8, p35
Focuses on the Bayesian statistics. Difference between Bayesian and frequentist statistics; Problems on probability elements; Neyman-Pearson system.
- Effects of policy-related variables on traffic fatalities: An extreme bounds analysis using... Fowles, Richard; Loeb, Peter D. // Southern Economic Journal;Oct95, Vol. 62 Issue 2, p359
Examines the fragility of various policy-related and socioeconomic variables in regression specifications using Bayesian extreme bounds analysis as developed in Leamer. Log-linear time series model.
- Bayesian statistical methods. Freedman, Laurence // BMJ: British Medical Journal (International Edition);9/7/96, Vol. 313 Issue 7057, p569
Editorial. Explains the basis of Bayesian statistical theory. Use of the theory in evaluating evidence from medical research; Alternative to a frequentist theory in drawing inferences from statistical data; Difference between Bayesian and frequentist theory.