Advance the filtering one time step, conditioning on the given evidence at the new time point.
Advance the filtering one time step, conditioning on the given evidence at the new time point.
Returns the distribution over the element referred to by the reference at the current time point.
Returns the distribution over the element referred to by the reference at the current time point.
Returns the expectation of the element referred to by the reference under the given function at the current time point.
Returns the expectation of the element referred to by the reference under the given function at the current time point.
Returns the probability that the element referred to by the reference satisfies the given predicate at the current time point.
Returns the probability that the element referred to by the reference satisfies the given predicate at the current time point.
The belief about the state of the system at the current point in time.
Called when the algorithm is killed.
Called when the algorithm is killed. By default, does nothing. Can be overridden.
Returns the distribution over the element referred to by the reference at the current time point.
Returns the distribution over the element referred to by the reference at the current time point.
Returns the expectation of the element referred to by the reference under the given function at the current time point.
Returns the expectation of the element referred to by the reference under the given function at the current time point.
Returns the probability that the element referred to by the reference satisfies the given predicate at the current time point.
Returns the probability that the element referred to by the reference satisfies the given predicate at the current time point.
Returns the probability that the element referred to by the reference produces the given value at the current time point.
Returns the probability that the element referred to by the reference produces the given value at the current time point.
Called when the algorithm is started before running any steps.
Called when the algorithm is started before running any steps. By default, does nothing. Can be overridden.
Kill the algorithm so that it is inactive.
Kill the algorithm so that it is inactive. It will no longer be able to provide answers.Throws AlgorithmInactiveException if the algorithm is not active.
Resume the computation of the algorithm, if it has been stopped.
Resume the computation of the algorithm, if it has been stopped. Throws AlgorithmInactiveException if the algorithm is not active.
Start the algorithm and make it active.
Start the algorithm and make it active. After it returns, the algorithm must be ready to provide answers. Throws AlgorithmActiveException if the algorithm is already active.
Stop the algorithm from computing.
Stop the algorithm from computing. The algorithm is still ready to provide answers after it returns. Throws AlgorithmInactiveException if the algorithm is not active.
An abstract class of particle filters. A particle filter is provided with three models: a static model, containing a universe defining a distribution over static elements that do not change over time; an initial model, containing a universe defining a distribution over the initial state of time-varying elements; and a transition model, which is a function from the previous universe to a new universe. defining the way the distribution over the new state of the time-varying variables depends on their values in the previous state. The fourth argument to the particle filter is the number of particles to use at each time step.
The particle filter works in an online fashion. At each point in time, it maintains its current beliefs about the state of the system as a set of representative states. advanceTime is used to move forward one time step. The particle filter updates its beliefs in light of the new evidence.