# msmtools.analysis.mfpt¶

msmtools.analysis.mfpt(T, target, origin=None, tau=1, mu=None)

Mean first passage times (from a set of starting states - optional) to a set of target states.

Parameters: T (ndarray or scipy.sparse matrix, shape=(n,n)) – Transition matrix. target (int or list of int) – Target states for mfpt calculation. origin (int or list of int (optional)) – Set of starting states. tau (int (optional)) – The time-lag (in elementary time steps of the microstate trajectory) at which the given transition matrix was constructed. mu ((n,) ndarray (optional)) – The stationary distribution of the transition matrix T. m_t – Mean first passage time or vector of mean first passage times. ndarray, shape=(n,) or shape(1,)

Notes

The mean first passage time $$\mathbf{E}_x[T_Y]$$ is the expected hitting time of one state $$y$$ in $$Y$$ when starting in state $$x$$.

For a fixed target state $$y$$ it is given by

$\begin{split}\mathbb{E}_x[T_y] = \left \{ \begin{array}{cc} 0 & x=y \\ 1+\sum_{z} T_{x,z} \mathbb{E}_z[T_y] & x \neq y \end{array} \right.\end{split}$

For a set of target states $$Y$$ it is given by

$\begin{split}\mathbb{E}_x[T_Y] = \left \{ \begin{array}{cc} 0 & x \in Y \\ 1+\sum_{z} T_{x,z} \mathbb{E}_z[T_Y] & x \notin Y \end{array} \right.\end{split}$

The mean first passage time between sets, $$\mathbf{E}_X[T_Y]$$, is given by

$\mathbb{E}_X[T_Y] = \sum_{x \in X} \frac{\mu_x \mathbb{E}_x[T_Y]}{\sum_{z \in X} \mu_z}$

References

 [1] Hoel, P G and S C Port and C J Stone. 1972. Introduction to Stochastic Processes.

Examples

>>> import numpy as np
>>> from msmtools.analysis import mfpt

>>> T = np.array([[0.9, 0.1, 0.0], [0.5, 0.0, 0.5], [0.0, 0.1, 0.9]])
>>> m_t = mfpt(T, 0)
>>> m_t
array([  0.,  12.,  22.])