# Difference between revisions of "Documentation:Monte Carlo Equilibration"

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|| observables | || observables | ||

|| - | || - | ||

− | || | + | || (list of ) Python str |

− | || | + | || (list of) measurement observable(s) |

|- | |- | ||

|| confidenceInterval | || confidenceInterval | ||

− | || 0. | + | || 0.63 |

− | || | + | || Python float |

− | || | + | || confidence interval in which steady state has not been reached |

|- | |- | ||

|| simplified | || simplified | ||

|| False | || False | ||

+ | || Python bool | ||

|| shall we combine the checks of all observables as 1 final boolean answer? | || shall we combine the checks of all observables as 1 final boolean answer? | ||

− | |||

|- | |- | ||

|| includeLog | || includeLog | ||

|| False | || False | ||

+ | || Python bool | ||

|| shall we print the detailed log? | || shall we print the detailed log? | ||

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|} | |} |

## Revision as of 14:27, 10 September 2013

## Contents

# Monte Carlo equilibration

## Theory

We have a timeseries of N measurements obtained from a Monte Carlo simulation, i.e. .

Suppose (s.t. ) is the least-squares best fitted line, we attempt to minimize w.r.t. and .

, :

### Slope of best-fitted line

### Error in slope of best-fitted line

Denoting , we have:

### Hypothesis testing

Using the standard z-test, we reject at confidence interval if

## Practice

### Synposis

pyalps.checkNonSteadyState(outfile, observables, confidenceInterval=0.63, simplified=False, includeLog=False)

argument | default | type | remark |

outfile | - | Python str | ALPS hdf5 output file(name) |

observables | - | (list of ) Python str | (list of) measurement observable(s) |

confidenceInterval | 0.63 | Python float | confidence interval in which steady state has not been reached |

simplified | False | Python bool | shall we combine the checks of all observables as 1 final boolean answer? |

includeLog | False | Python bool | shall we print the detailed log? |