scholarly journals When mechanism matters: Bayesian forecasting using models of ecological diffusion

2017 ◽  
Vol 20 (5) ◽  
pp. 640-650 ◽  
Author(s):  
Trevor J. Hefley ◽  
Mevin B. Hooten ◽  
Robin E. Russell ◽  
Daniel P. Walsh ◽  
James A. Powell
Keyword(s):  
Bayesian Forecasting ◽  
Ecological Diffusion

Applied Bayesian Forecasting and Time Series Analysis.

1996 ◽  
Vol 45 (1) ◽  
pp. 136
Author(s):  
A. V. Metcalfe ◽  
A. Pole ◽  
M. West ◽  
J. Harrison
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Bayesian Forecasting ◽  
Series Analysis

Bayesian Forcasting Model – 2020 Presidential Elections in the United States of America

2021 ◽  
Vol 704 (1) ◽  
pp. 91-104
Author(s):  
Maria Raczyńska
Keyword(s):  
United States ◽  
United States Of America ◽  
Presidential Elections ◽  
Bayesian Models ◽  
The United States ◽  
Point Of View ◽  
Forecasting Model ◽  
Bayesian Forecasting ◽  
Technical Point ◽  
Made In

The article describes and explains a prior centric Bayesian forecasting model for the 2020 US elections.The model is based on the The Economist forecasting project, but strongly differs from it. From the technical point of view, it uses R and Stan programming and Stan software. The article’s focus is on theoretical decisions made in the process of constructing the model and outcomes. It describes why Bayesian models are used and how they are used to predict US presidential elections.

On Bayesian forecasting of procurement delays: a case study

2006 ◽  
Vol 22 (2) ◽  
pp. 181-192 ◽  
Author(s):  
Jesus Palomo ◽  
Fabrizio Ruggeri ◽  
David Rios Insua ◽  
Enrico Cagno ◽  
Franco Caron ◽  
...  
Keyword(s):  
Bayesian Forecasting

Bayesian Forecasting of Cohort Fertility

2014 ◽  
Vol 109 (506) ◽  
pp. 500-513 ◽  
Author(s):  
Carl Schmertmann ◽  
Emilio Zagheni ◽  
Joshua R. Goldstein ◽  
Mikko Myrskylä
Keyword(s):  
Bayesian Forecasting ◽  
Cohort Fertility

Evaluation of a Two-Compartment Bayesian Forecasting Program for Predicting Vancomycin Concentrations

1989 ◽  
Vol 11 (3) ◽  
pp. 269-275 ◽  
Author(s):  
Keith A. Rodvold ◽  
Randy D. Pryka ◽  
Mark Garrison ◽  
John C. Rotschafer
Keyword(s):  
Bayesian Forecasting

Capability of the Bayesian Forecasting Method to Predict Field Time Series

2018 ◽  
Vol 140 (12) ◽  
Author(s):  
Nicolò Gatta ◽  
Mauro Venturini ◽  
Lucrezia Manservigi ◽  
Giuseppe Fabio Ceschini ◽  
Giovanni Bechini
Keyword(s):  
Time Series ◽  
Prediction Error ◽  
Field Data ◽  
Single Step ◽  
Bayesian Forecasting ◽  
Virtual Sensors ◽  
Average Prediction Error ◽  
Prediction Scheme ◽  
Forecasting Method ◽  
Prediction Capability

This paper addresses the challenge of forecasting the future values of gas turbine measureable quantities. The final aim is the simulation of “virtual sensors” capable of producing statistically coherent measurements aimed at replacing anomalous observations discarded from the time series. Among the different available approaches, the Bayesian forecasting method (BFM) adopted in this paper uses the information held by a pool of observations as knowledge base to forecast the values at a future state. The BFM algorithm is applied in this paper to Siemens field data to assess its prediction capability, by considering two different approaches, i.e., single-step prediction (SSP) and multistep prediction (MSP). While SSP predicts the next observation by using true data as base of knowledge, MSP uses previously predicted data as base of knowledge to perform the prediction of future time steps. The results show that BFM single-step average prediction error can be very low, when filtered field data are analyzed. On the contrary, the average prediction error achieved in case of BFM multistep prediction is remarkably higher. To overcome this issue, the BFM single-step prediction scheme is also applied to clusters of time-wise averaged data. In this manner, an acceptable average prediction error can be achieved by considering clusters composed of 60 observations.

scholarly journals External validation of the predictive performance of population pharmacokinetic models for phenobarbital in pediatric patients

2020 ◽  
Author(s):  
Sunae Ryu ◽  
Woo Jin Jung ◽  
Zheng Jiao ◽  
Jung Woo Chae ◽  
Hwi-yeol Yun
Keyword(s):  
External Validation ◽  
Predictive Performance ◽  
Validation Dataset ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic ◽  
Pharmacokinetic Models ◽  
Bayesian Forecasting ◽  
Predictive Capability ◽  
Young Infants ◽  
Simulation Based

Aim: Several studies have reported population pharmacokinetic models for phenobarbital (PB), but the predictive performance of these models has not been well documented. This study aims to do external validation of the predictive performance in published pharmacokinetic models. Methods: Therapeutic drug monitoring data collected in neonates and young infants treated with PB for seizure control, was used for external validation. A literature review was conducted through PubMed to identify population pharmacokinetic models. Prediction- and simulation-based diagnostics, and Bayesian forecasting were performed for external validation. The incorporation of size or maturity functions into the published models was also tested for prediction improvement. Results: A total of 79 serum concentrations from 28 subjects were included in the external validation dataset. Seven population pharmacokinetic studies of PB were selected for evaluation. The model by Voller et al. [27] showed the best performance concerning prediction-based evaluation. In simulation-based analyses, the normalized prediction distribution error of two models (those of Shellhaas et al. [24] and Marsot et al. [25]) obeyed a normal distribution. Bayesian forecasting with more than one observation improved predictive capability. Incorporation of both allometric size scaling and maturation function generally enhanced the predictive performance, but with marked improvement for the adult pharmacokinetic model. Conclusion: The predictive performance of published pharmacokinetic models of PB was diverse, and validation may be necessary to extrapolate to different clinical settings. Our findings suggest that Bayesian forecasting improves the predictive capability of individual concentrations for pediatrics.

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