make.lik: Observation Process Distribution Function
In CollocInfer: Collocation Inference for Dynamic Systems

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.

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make.SSElik()

make.multinorm()

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 `fn` in `more`) and observations
`make.Multinorm`	calculates a multivariate normal distribution.

LS.setup, multinorm.setup

# 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())