scholarly journals Bayesian model discrimination for partially-observed epidemic models

2019 ◽  
Author(s):  
James N. Walker ◽  
Andrew J. Black ◽  
Joshua V. Ross
Keyword(s):  
Model Selection ◽  
Importance Sampling ◽  
Continuous Time ◽  
Bayesian Model ◽  
Broad Class ◽  
Infectious Period ◽  
Importance Sampling Method ◽  
Markov Chain Models ◽  
Wide Range ◽  
Partially Observed

AbstractAn efficient method for Bayesian model selection is presented for a broad class of continuous-time Markov chain models and is subsequently applied to two important problems in epidemiology. The first problem is to identify the shape of the infectious period distribution; the second problem is to determine whether individuals display symptoms before, at the same time, or after they become infectious. In both cases we show that the correct model can be identified, in the majority of cases, from symptom onset data generated from multiple outbreaks in small populations. The method works by evaluating the likelihood using a particle filter that incorporates a novel importance sampling algorithm designed for partially-observed continuous-time Markov chains. This is combined with another importance sampling method to unbiasedly estimate the model evidence. These come with estimates of precision, which allow for stopping criterion to be employed. Our method is general and can be applied to a wide range of model selection problems in biological and epidemiological systems with intractable likelihood functions.

Bayesian model selection for partially observed diffusion models

Biometrika ◽  
2006 ◽  
Vol 93 (4) ◽  
pp. 809-825 ◽  
Author(s):  
Petros Dellaportas ◽  
Nial Friel ◽  
Gareth O. Roberts
Keyword(s):  
Model Selection ◽  
Bayesian Model ◽  
Bayesian Model Selection ◽  
Diffusion Models ◽  
Partially Observed ◽  
Selection For

MODEL SELECTION AND AVERAGING OF HEALTH COSTS IN EPISODE TREATMENT GROUPS

2016 ◽  
Vol 47 (1) ◽  
pp. 153-167 ◽  
Author(s):  
Shujuan Huang ◽  
Brian Hartman ◽  
Vytaras Brazauskas
Keyword(s):  
Random Forest ◽  
Model Selection ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Cost Model ◽  
Model Averaging ◽  
Large Data ◽  
Data Set ◽  
Treatment Groups ◽  
Wide Range

Episode Treatment Groups (ETGs) classify related services into medically relevant and distinct units describing an episode of care. Proper model selection for those ETG-based costs is essential to adequately price and manage health insurance risks. The optimal claim cost model (or model probabilities) can vary depending on the disease. We compare four potential models (lognormal, gamma, log-skew-t and Lomax) using four different model selection methods (AIC and BIC weights, Random Forest feature classification and Bayesian model averaging) on 320 ETGs. Using the data from a major health insurer, which consists of more than 33 million observations from 9 million claimants, we compare the various methods on both speed and precision, and also examine the wide range of selected models for the different ETGs. Several case studies are provided for illustration. It is found that Random Forest feature selection is computationally efficient and sufficiently accurate, hence being preferred in this large data set. When feasible (on smaller data sets), Bayesian model averaging is preferred because of the posterior model probabilities.

scholarly journals American Option Pricing with Importance Sampling and Shifted Regressions

2021 ◽  
Vol 14 (8) ◽  
pp. 340
Author(s):  
Francois-Michel Boire ◽  
R. Mark Reesor ◽  
Lars Stentoft
Keyword(s):  
Importance Sampling ◽  
Mean Squared Error ◽  
Importance Measure ◽  
Option Prices ◽  
American Option Pricing ◽  
Early Exercise ◽  
Importance Sampling Method ◽  
Wide Range ◽  
Estimator Bias ◽  
Simulation And Regression

This paper proposes a new method for pricing American options that uses importance sampling to reduce estimator bias and variance in simulation-and-regression based methods. Our suggested method uses regressions under the importance measure directly, instead of under the nominal measure as is the standard, to determine the optimal early exercise strategy. Our numerical results show that this method successfully reduces the bias plaguing the standard importance sampling method across a wide range of moneyness and maturities, with negligible change to estimator variance. When a low number of paths is used, our method always improves on the standard method and reduces average root mean squared error of estimated option prices by 22.5%.

The Argument from (A) to (Y)

2018 ◽  
Author(s):  
Ted Poston
Keyword(s):  
Bayesian Model ◽  
Independent Evidence ◽  
Strength Of Evidence ◽  
Wide Range ◽  
Simple Summation

This chapter provides a Bayesian model of strength of evidence in cases in which there are multiple items of independent evidence. The author uses this Bayesian model to evaluate the strength of evidence for theism if, as Plantinga claims, there are two dozen or so arguments for theism. The model turns questions of the overall strength of multiple arguments into a simple summation problem. Moreover, it provides a clear framework for advancing questions about how relationships between the arguments bear on the overall strength of evidence for theism. The Bayesian model developed in this chapter has a wide-range of applications for modeling strength of evidence in cumulative case arguments.

The application of continuous‐time Markov chain models in the analysis of choice flume experiments

2021 ◽  
Author(s):  
Michael A. Spence ◽  
Evalyne W. Muiruri ◽  
David L. Maxwell ◽  
Scott Davis ◽  
Dave Sheahan
Keyword(s):  
Markov Chain ◽  
Continuous Time ◽  
Continuous Time Markov Chain ◽  
Flume Experiments ◽  
Markov Chain Models

The (in)stability of Bayesian model selection criteria in disease mapping

2021 ◽  
Vol 43 ◽  
pp. 100502
Author(s):  
M. Vranckx ◽  
T. Neyens ◽  
C. Faes
Keyword(s):  
Model Selection ◽  
Bayesian Model ◽  
Selection Criteria ◽  
Disease Mapping ◽  
Bayesian Model Selection ◽  
Model Selection Criteria

scholarly journals Bayesian model selection on scalar ε -field dark energy

2021 ◽  
Vol 103 (4) ◽  
Author(s):  
J. Alberto Vázquez ◽  
David Tamayo ◽  
Anjan A. Sen ◽  
Israel Quiros
Keyword(s):  
Dark Energy ◽  
Model Selection ◽  
Bayesian Model ◽  
Bayesian Model Selection

scholarly journals Accounting for cell lineage and sex effects in the identification of cell-specific DNA methylation using a Bayesian model selection algorithm

PLoS ONE ◽  
2017 ◽  
Vol 12 (9) ◽  
pp. e0182455 ◽  
Author(s):  
Nicole White ◽  
Miles Benton ◽  
Daniel Kennedy ◽  
Andrew Fox ◽  
Lyn Griffiths ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Model Selection ◽  
Bayesian Model ◽  
Cell Lineage ◽  
Bayesian Model Selection ◽  
Selection Algorithm ◽  
Sex Effects
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