object ParticleGibbs
Particle Gibbs Sampler for A Dynamic Generalised Linear Model
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def
filter(n: Int, p: Parameters, mod: Model, obs: List[Data])(state: List[(Double, DenseVector[Double])]): Rand[(Double, List[(Double, DenseVector[Double])])]
Run the Particle Gibbs Filter, given a samples value of the state
Run the Particle Gibbs Filter, given a samples value of the state
- n
the number of particles in the filter
- p
the parameters used to run the filter
- mod
the specification of the system evolution matrix, G and observation matrix F
- obs
a list of observations
- state
the conditioned upon state
- returns
a tuple containing the log-likelihood of the parameters given the observations and a single state path deterministically chosen to be the final path
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- def initState(p: Parameters): MultivariateGaussianSvd
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def
sampleState(states: List[List[(Double, DenseVector[Double])]], weights: List[Double]): Rand[List[(Double, DenseVector[Double])]]
Using the weights at time T (the end of all observations) sample a path from the collection of paths
Using the weights at time T (the end of all observations) sample a path from the collection of paths
- states
a collection of paths with ancestory, the outer list is of length T, inner length N
- weights
particle weights at time T
- returns
a single path
- def step(mod: Model, p: Parameters): (State, (Data, DenseVector[Double])) ⇒ State
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