sde.prior: SDE prior function.
In msde: Bayesian Inference for Multivariate Stochastic Differential Equations

Description Usage Arguments Details Value Examples

Evaluates the SDE prior given data, parameter, and hyperparameter values.

1	sde.prior(model, theta, x, hyper)

`model`	An `sde.model` object.
`theta`	A vector or matrix of parameters with `nparams` columns.
`x`	A vector or matrix of data with `ndims` columns.
`hyper`	The hyperparameters of the SDE prior. See Details.

The prior is constructed at the C++ level by defining a function (i.e., public member)

1	double logPrior(double theta, double x)

within the sdePrior class. At the R level, the hyper.check argument of sde.make.model() is a function with arguments hyper, param.names, data.names used to convert hyper into a list of NULL or double-vectors which get passed on to the C++ code. This function can also be used to throw R-level errors to protect the C++ code from invalid inputs, as is done for the default prior in mvn.hyper.check(). For a full example see the "Custom Prior" section in vignette("msde-quicktut").

A vector of log-prior densities evaluated at the inputs.

hmod <- sde.examples("hest") # load Heston's model

# setting prior for 3 parameters
rv.names <- c("alpha","gamma","rho")
mu <- rnorm(3)
Sigma <- crossprod(matrix(rnorm(9),3,3))
names(mu) <- rv.names
colnames(Sigma) <- rv.names
rownames(Sigma) <- rv.names
hyper <- list(mu = mu, Sigma = Sigma)

# Simulate data
nreps <- 10
theta <- c(alpha = 0.1, gamma = 1, beta = 0.8, sigma = 0.6, rho = -0.8)
x0 <- c(X = log(1000), Z = 0.1)
Theta <- apply(t(replicate(nreps,theta)),2,jitter)
X0 <- apply(t(replicate(nreps,x0)),2,jitter)

sde.prior(model = hmod, x = X0, theta = Theta, hyper = hyper)