Proposes new coefficients in a spectral GP object as normal deviates
centered around the current values, with the proposal standard deviation the
product of the supplied standard deviation(s) and the square root of
the prior variances. The proposal can be done for all coefficients at
once (`block=0`

) or for individual blocks.

1 2 | ```
## S3 method for class 'gp'
propose.coeff(object, block = 0, proposal.sd = 1,...)
``` |

`object` |
A GP object, created by |

`block` |
The block of coefficients to be proposed, or 0 if all coefficients are to be proposed. |

`proposal.sd` |
Proposal standard deviation. This is multiplied by the square root of the prior variance for each coefficient to produce the final proposal standard deviation. |

`...` |
Other arguments. |

This function can be used to simulate a GP by using
`proposal.sd=1`

to sample coefficients, `coeff`

, from the
prior or in a MCMC context to propose new coefficient
values via the Metropolis algorithm. The function automatically
updates the process values in the `process`

component of the gp
list based on the new coefficient values.

The function modifies the GP object, which is essentially a pointer (an R environment in this case), so NULL is returned.

Christopher Paciorek paciorek@alumni.cmu.edu

Type 'citation("spectralGP")' for references.

`gp`

, `calc.variances.gp`

, `simulate.gp`

, `updateprocess.gp`

,
`zero.coeff.gp`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
library(spectralGP)
rho=1
gp1=gp(128,matern.specdens,c(rho,4))
gp2=gp(c(64,64),matern.specdens,c(rho,4))
propose.coeff(gp1)
propose.coeff(gp2)
plot(gp1)
plot(gp2)
prior1=logdensity(gp1)
prior2=logdensity(gp2)
add.blocks(gp1)
add.blocks(gp2)
propose.coeff(gp1,block=2,proposal.sd=0.1)
propose.coeff(gp2,block=3,proposal.sd=0.1)
priorstar1=logdensity(gp1)
priorstar2=logdensity(gp2)
plot(gp1)
plot(gp2)
``` |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

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