# M step of the EM algorithm for fitting homogeneous Markov switching auto-regressive models.

### Description

M step of the EM algorithm for fitting homogeneous Markov switching auto-regressive models, called in fit.MSAR.

### Usage

1 | ```
Mstep.hh.MSAR(data, theta, FB)
``` |

### Arguments

`data` |
array of univariate or multivariate series with dimension T*N.samples*d. T: number of time steps of each sample, N.samples: number of realisations of the same stationary process, d: dimension. |

`theta` |
model's parameter; object of class MSAR. See also init.theta.MSAR. |

`FB` |
Forward-Backward results, obtained by calling Estep.MSAR function |

### Value

A list containing

`A0` |
intercepts |

`A` |
AR coefficients |

`sigma` |
variance of innovation |

`prior` |
prior probabilities |

`transmat` |
transition matrix |

### Author(s)

Valerie Monbet, valerie.monbet@univ-rennes1.fr

### References

Ailliot P., Monbet V., (2012), Markov switching autoregressive models for wind time series. Environmental Modelling & Software, 30, pp 92-101.

### See Also

fit.MSAR, Estep.MSAR, Mstep.classif