View source: R/kernel_normal.R

kernel_normal | R Documentation |

Gaussian Transition Kernel

```
kernel_normal(mu = 0, scale = 1, fixed = FALSE, scheme = "joint")
kernel_normal_reflective(
mu = 0,
scale = 1,
lb = -.Machine$double.xmax,
ub = .Machine$double.xmax,
fixed = FALSE,
scheme = "joint"
)
```

`mu, scale` |
Either a numeric vector or a scalar. Proposal mean and scale. If scalar, values are recycled to match the number of parameters in the objective function. |

`fixed, scheme` |
For multivariate functions, sets the update plan.
See |

`lb, ub` |
Either a numeric vector or a scalar. Lower and upper bounds for bounded kernels. When of length 1, the values are recycled to match the number of parameters in the objective function. |

The `kernel_normal`

function provides the canonical normal kernel
with symmetric transition probabilities.

The `kernel_normal_reflective`

implements the normal kernel with reflective
boundaries. Lower and upper bounds are treated using reflecting boundaries, this is,
if the proposed `\theta'`

is greater than the `ub`

, then `\theta' - ub`

is subtracted from `ub`

. At the same time, if it is less than `lb`

, then
`lb - \theta'`

is added to `lb`

iterating until `\theta`

is within
`[lb, ub]`

.

In this case, the transition probability is symmetric (just like the normal kernel).

An object of class fmcmc_kernel. `fmcmc_kernel`

objects are intended
to be used with the `MCMC()`

function.

Other kernels:
`kernel_adapt()`

,
`kernel_mirror`

,
`kernel_new()`

,
`kernel_ram()`

,
`kernel_unif()`

```
# Normal kernel with a small scale (sd) of 0.05
kern <- kernel_normal(scale = 0.05)
# Using boundaries for the second parameter of a two parameter chain
# to have values in [0, 100].
kern <- kernel_normal_reflective(
ub = c(.Machine$double.xmax, 100),
lb = c(-.Machine$double.xmax, 0)
)
```

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