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bdpopt (version 1.0-1)

Optimisation of Bayesian Decision Problems

Description

Optimisation of the expected utility in single-stage and multi-stage Bayesian decision problems. The expected utility is estimated by simulation. For single-stage problems, JAGS is used to draw MCMC samples.

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Version

Install

install.packages('bdpopt')

Monthly Downloads

26

Version

1.0-1

License

GPL-2

Maintainer

Sebastian Jobjrnsson

Last Published

March 30th, 2016

Functions in bdpopt (1.0-1)

print.sim.model

Print Status Of Simulation Model Object
eval.eu.sim.model

Evaluate Expected Utility
fit.gpr.sim.model

Fit A Gaussian Process Regression Function
fit.loess

Fit A Local Polynomial Regression Function
diag.mcmc.list.sim.model

MCMC List For Diagnostic Evaluation
plot.sim.model

Plot The Results Contained In Simulation Model Object
sequential.dp

Construct A Sequential Decision Problem
optimise.eu.sim.model

Optimise Expected Utility
diag.mcmc.list

MCMC List For Diagnostic Evaluation
create.utility.function

Create Utility Function For The Normal Model
n.opt

Optimise A Simple Normal Model
optimise.sequential.normal.eu

Optimise A Sequential Normal Decision Problem
sequential.normal.dp

Create A Sequential Normal Decision Problem
fit.loess.sim.model

Fit A Local Polynomial Regression Function
eval.on.grid

Evaluate Expected Utility On A Grid
sim.model

Construct A Simulation Model Object
eval.on.grid.sim.model

Evaluate Expected Utility On A Grid
optimise.sequential.eu

Optimise Sequential Expected Utility
create.normal.model.from.file

Create Normal Emax Model From File
eval.eu

Evaluate Expected Utility
create.normal.model

Create Normal Emax Model
optimise.eu

Optimise Expected Utility
fit.gpr

Fit A Gaussian Process Regression Function