# sv_prior: Prior Distributions in 'stochvol' In stochvol: Efficient Bayesian Inference for Stochastic Volatility (SV) Models

## Description

The functions below can be supplied to `specify_priors` to overwrite the default set of prior distributions in `svsample`. The functions have `mean`, `range`, `density`, and `print` methods.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```sv_constant(value) sv_normal(mean = 0, sd = 1) sv_multinormal(mean = 0, precision = NULL, sd = 1, dim = NA) sv_gamma(shape, rate) sv_inverse_gamma(shape, scale) sv_beta(shape1, shape2) sv_exponential(rate) sv_infinity() ```

## Arguments

 `value` The constant value for the degenerate constant distribution `mean` Expected value for the univariate normal distribution or mean vector of the multivariate normal distribution `sd` Standard deviation for the univariate normal distribution or constant scale of the multivariate normal distribution `precision` Precision matrix for the multivariate normal distribution `dim` (optional) Dimension of the multivariate distribution `shape` Shape parameter for the distribution `rate` Rate parameter for the distribution `scale` Scale parameter for the distribution `shape1` First shape parameter for the distribution `shape2` Second shape parameter for the distribution

## Multivariate Normal

Multivariate normal objects can be specified several ways. The most general way is by calling `sv_multinormal(mean, precision)`, which allows for arbitrary mean and (valid) precision arguments. Constant mean vectors and constant diagonal precision matrices of dimension `D` can be created two ways: either `sv_multinormal(mean, sd, dim = D)` or `rep(sv_normal(mean, sd), length.out = D)`.

Other priors: `specify_priors()`