Description Usage Details Value See Also Examples

Returns a list of functions that calculate the observation process distribution and its derivatives; designed to be used with the collocation inference functions.

1 2 3 |

These functions require `more`

to be a list with elements:

`fn`

The transform function of the states to observations, or to their derivatives.`dfdx`

The derivative of`fn`

with respect to states.`dfdp`

The derivative of`fn`

with respect to parameters.`d2fdx2`

The second derivative of`fn`

with respect to states.`d2fdxdp`

The cross derivative of`fn`

with respect to states and parameters.

`make.Multinorm`

further requires:

`var.fn`

The variance given in terms of states and parameters.`var.dfdx`

The derivative of`var.fn`

with respect to states.`var.dfdp`

The derivative of`var.fn`

with respect to parameters.`var.d2fdx2`

The second derivative of`var.fn`

with respect to states.`var.d2fdxdp`

The cross derivative of`var.fn`

with respect to states and parameters.

`make.SSElik`

further requres `weights`

giving weights to each observation.

A list of functions that calculate the log observation distribution and its derivatives.

`make.SSElik` |
calculates weighted squared error between predictions
(given by |

`make.Multinorm` |
calculates a multivariate normal distribution. |

1 2 3 4 5 6 7 8 9 | ```
# Straightforward sum of squares:
lik = make.SSElik()
lik$more = make.id()
# Multivariate normal about an exponentiated state with constant variance
lik = make.multinorm()
lik$more = c(make.exp(),make.cvar())
``` |

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