object Mll
Functions associated with particle filtering of Markov process models against time series data and the computation of marginal model likelihoods.
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- def bpf[S, O](x0: Vector[S], t0: Time, data: Ts[O], dataLik: (S, O) => LogLik, stepFun: (S, Time, Time) => S)(implicit arg0: State[S]): (LogLik, Time, Vector[S])
A simple bootstrap particle filter with multinomial resampling.
A simple bootstrap particle filter with multinomial resampling. Note that it supports time series of observations on irregular time grids. Note that this is the function which does most of the work for
pfMll.- x0
A sample from the prior on the state at time
t0. The size of the sample determines the number of particles used in the particle filter.- t0
The time corresponding to the prior sample
- data
The observed data in the form of a time series
- dataLik
The log-likelihood of the observation model
- stepFun
The transition kernel of the underlying process
- returns
A triple: the first element is the log of an unbiased estimate of the marginal likelihood of the model, the second is just the final time point, and the third is a sample from the posterior distribution of the final time point.
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- def pfMll[P, S, O](simX0: (P) => Vector[S], t0: Time, stepFun: (P) => (S, Time, Time) => S, dataLik: (P) => (S, O) => LogLik, data: Ts[O])(implicit arg0: State[S]): (P) => LogLik
A function for evaluating marginal log-likelihoods of parametrised models.
A function for evaluating marginal log-likelihoods of parametrised models. It basically just threads a parameter value through the arguments required for
bpf.- simX0
A function which when provided with a parameter value will return a sample from the prior on the intial state of a Markov process model. The size of the sample will determine the number of particles used in the particle filter.
- t0
The intial time corresponding to the prior sample
- stepFun
A function, which when provided with a parameter value, will return the transition kernel of a Markov process.
- dataLik
A function, which when provided with a parameter value, will return the log-likelihood of the observation model
- data
The observed data in the form of a time series
- def sample(n: Int, prob: DenseVector[Double]): Vector[Int]
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