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

## Description

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

## Usage

 ```1 2 3``` ```make.SSElik() make.multinorm() ```

## Details

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.

## Value

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`
 ```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()) ```