SimData
Related Doc:
package model
Forecast data
Forecast data
the time of the observation
an observation of the process
the upper and lower credible intervals of the observation
the transformed latent state
the credible intervals of the transformed latent state
the untransformed latent state
the intervals of the latent state
Given a stream of times, a model an initial sample of states and the associated time of the state sample generate a forecast include credible intervals in a stream
Given a stream of times, a model an initial sample of states and the associated time of the state sample generate a forecast include credible intervals in a stream
a vector of state realisations at time t0
an initial time which the state was realised at
the model to simulate from
a flow object which can be attached to a Source[Time] and a suitable Sink
Performs a one step ahead forecast for any time ahead in the future, given a sample of states, time of the state sample the time to forecast to and a model
Performs a one step ahead forecast for any time ahead in the future, given a sample of states, time of the state sample the time to forecast to and a model
a sample of states at the same time point, t0
the time the sample of states was made
the time to forcast ahead until
the model to simulate from
a tuple, including a ForecastOut object and the vector of states which has been advanced by dt = t - t0
Simulate data from a list of times, allowing for irregular observations
Simulate data from a list of times, allowing for irregular observations
a list of times to simulate observations at
an exponential family model
a sequence of Data
Given an initial state, simulate from a model
Given an initial state, simulate from a model
the initial state
a list of times to simulate the data
an exponential family model
a sequence of Data
Simulate a vector of Data using Rand (a monad representing a distribution which can be sampled from)
Simulate a vector of Data using Rand (a monad representing a distribution which can be sampled from)
a list of times to observe the process at
an exponential family model
a Rand monad containing a vector of data
Simulate the log-Gaussian Cox-Process using thinning
Simulate the log-Gaussian Cox-Process using thinning
the starting time of the process
the model to simulate from. In a composition, the LogGaussianCox must be the left-hand model
an integer specifying the timestep between simulating the latent state, 10e-precision
a vector of Data specifying when events happened
Generates a vector of event times from the Log-Gaussian Cox-Process by thinning an exponential process, returns the value of the state space at the event times only
Generates a vector of event times from the Log-Gaussian Cox-Process by thinning an exponential process, returns the value of the state space at the event times only
the start time of the process
the end time of the process
the model used to generate the events. In a composition, LogGaussiancox must be the left-hand model
the size of the discretized grid to simulate the latent state on, 10e-precision
a vector of Data at representing only the times which events happened
Simulates a diffusion process at any specified times
Simulates a diffusion process at any specified times
the initial state
a list of times at which to simulate the diffusion process
the transition kernel of a diffusion process which can be simulated from
a vector of Sde with values and times
Simulate a diffusion process as a stream
Simulate a diffusion process as a stream
the starting value of the stream
the starting time of the stream
the ending time of the stream
the step size of the stream 10e(-precision)
the stepping function to use to generate the SDE Stream
a lazily evaluated stream of Sde
Simulate a single step of an exponential family model
Simulate a single step of an exponential family model
the initial state at the time of the last observation
the time of the last observation
the time increment to the next observation
a composed model, not the LogGaussiancox model
a Data element with the next latent state and observation
Simulate a step using Rand (a monad representing a distribution which can be sampled from)
Simulate a step using Rand (a monad representing a distribution which can be sampled from)
the initial state at the time of the previous observation
the time of the previous observation
the time increment to the time of the next observation
an exponential family model to simulate from
a Rand monad containing data, which can be sampled from to give a realisation from a model
Simulate data as an Akka Stream, with regular time intervals
Simulate data as an Akka Stream, with regular time intervals
The model to simulate from, can be composed or single
Used to determine the step length, dt = 10^-precision
the starting time of the process
a source of an Akka Stream