`FeynmanKacParticleFilters.create_transition_kernels`

— Method`create_transition_kernels(data, transition_kernel, prior)`

Creates a dictionary with observation times as keys and transition kernels as values. This assumes that the transition kernel only depends on the difference between observation times. Values are functions which take a state as argument and return a random state obtained through the transition kernel.

**Arguments**

`data::Dict{Real, Any}`

: keys are observation times, values are observed data.`transition_kernel::Function`

: a function that takes time difference as argument and returns a transition kernel (a function which takes a state as argument and return a random state obtained through the transition kernel).`prior::Distribution`

: a prior distribution that can be dispatched to rand().

**Examples**

```
julia> bar([1, 2], [1, 2])
1
```

`FeynmanKacParticleFilters.normalise`

— MethodNormalises a vector