Step
Functions which accept a Spn and return a function for simulating from the transition kernel of that model
Attributes
- Graph
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- Supertypes
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class Objecttrait Matchableclass Any
- Self type
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Step.type
Members list
Value members
Concrete methods
An Euler-Maruyama simulation of a CLE approximation to the provided Spn.
An Euler-Maruyama simulation of a CLE approximation to the provided Spn.
Value parameters
- dt
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The internal time step of the algorithm. Not the same as the
deltatof the returned transition kernel. - n
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A
Spn[DoubleState]model (note that the state must be continous)
Attributes
- Returns
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A function with type signature
(x0: DoubleState, t0: Time, deltat: Time) => DoubleStatewhich will simulate the state of the system at timet0+deltatgiven initial statex0and intial timet0
A simple Euler integration of the continuous deterministic approximation to the provided Spn. Euler methods are well-known to be very unstable, but the function can be useful for getting a basic idea of how the model behaves in the absence of noise.
A simple Euler integration of the continuous deterministic approximation to the provided Spn. Euler methods are well-known to be very unstable, but the function can be useful for getting a basic idea of how the model behaves in the absence of noise.
Value parameters
- dt
-
The internal time step of the algorithm. Not the same as the
deltatof the returned transition kernel. - n
-
A
Spn[DoubleState]model (note that the state must be continous)
Attributes
- Returns
-
A function with type signature
(x0: DoubleState, t0: Time, deltat: Time) => DoubleStatewhich will simulate the state of the system at timet0+deltatgiven initial statex0and intial timet0
The Gillespie algorithm, sometimes known as the direct method, or the stochastic simulation algorithm (SSA)
The Gillespie algorithm, sometimes known as the direct method, or the stochastic simulation algorithm (SSA)
Value parameters
- maxH
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Threshold for terminating simulation early
- minH
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Threshold for treating hazard as zero
- n
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A
Spn[IntState]model
Attributes
- Returns
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A function with type signature
(x0: IntState, t0: Time, deltat: Time) \=> IntStatewhich will simulate the state of the system at timet0+deltatgiven initial statex0and intial timet0
A Poisson time-stepping algorithm. Like a tau-leaping algorithm, but with fixed step sizes.
A Poisson time-stepping algorithm. Like a tau-leaping algorithm, but with fixed step sizes.
Value parameters
- dt
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The internal time step of the algorithm. Not the same as the
deltatof the returned transition kernel. - n
-
A
Spn[IntState]model
Attributes
- Returns
-
A function with type signature
(x0: IntState, t0: Time, deltat: Time) \=> IntStatewhich will simulate the state of the system at timet0+deltatgiven initial statex0and intial timet0