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com.github.jonnylaw.model

StateSpace

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object StateSpace

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  15. def stepBrownian(p: SdeParameter): (State, TimeIncrement) ⇒ Rand[State]

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    A step function for generalised brownian motion, dx_t = mu dt + sigma dW_t

    A step function for generalised brownian motion, dx_t = mu dt + sigma dW_t

    p

    an sde parameter

    returns

    A function from state, time increment to state

  16. def stepConstant(p: SdeParameter): (State, TimeIncrement) ⇒ Rand[State]

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    Steps the state by the value of the parameter "a" multiplied by the time increment "dt"

    Steps the state by the value of the parameter "a" multiplied by the time increment "dt"

    p

    a parameter Map

    returns

    a function from (State, dt) => State, with the states being the same structure before and after

  17. def stepNull(p: Parameters): (State, TimeIncrement) ⇒ Rand[State]

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    Steps all the states using the identity

    Steps all the states using the identity

    p

    a Parameter

    returns

    a function from state, dt => State

  18. def stepOrnstein(p: SdeParameter): (State, TimeIncrement) ⇒ Rand[State]

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    A step function for the Ornstein Uhlenbeck process dx_t = alpha(theta - x_t) dt + sigma dW_t

    A step function for the Ornstein Uhlenbeck process dx_t = alpha(theta - x_t) dt + sigma dW_t

    p

    the parameters of the ornstein uhlenbeck process, theta, alpha and sigma

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