pred.base: Bayesian prediction function

In SimoneHermann/BaPreStoPro: Bayesian Prediction of Stochastic Processes

Description

Drawing from predictive distribution based on distribution function Fun(x, x0, samples) or density dens(x, x0, samples). Samples should contain samples from the posterior distribution of the parameters.

Usage

pred.base(samples, Fun, dens, len = 100, x0, method = c("vector", "free"),
  pred.alg = c("Distribution", "Trajectory"), sampling.alg = c("RejSamp",
  "InvMethod"), candArea, grid = 0.001)

Arguments

samples	MCMC samples
Fun	cumulative distribution function
dens	density function
len	number of samples to be drawn
x0	vector of starting points
method	vectorial ("vector") or not ("free")
pred.alg	prediction algorithm, "Distribution" or "Trajectory"
sampling.alg	sampling algorithm, rejection sampling ("RejSamp") or inversion method ("InvMethod")
candArea	candidate area
grid	fineness degree

Value

vector of samples from prediction

References

Hermann, S. (2016). Bayesian Prediction for Stochastic Processes based on the Euler Approximation Scheme. SFB 823 discussion paper 27/16.

SimoneHermann/BaPreStoPro documentation built on May 9, 2019, 1:46 p.m.