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Analyzing Monte-Carlo data

Mean

There is a range of functions that allow the calculation of statistical properties of Monte-Carlo data.

In general, when running a simulation, the results are recorded with an Observable-class and stored in a HDF5 file. These files can then later be read and statistical properties can be calculated. These results can also be written back into the file. There are several examples on how to do this in the examples/alea folder.

Here is an overview of the functions available

Function Name

Argument(s)

Options

Return Type

mean

Timeseries

None

AverageType

variance

Timeseries

None

AverageType

error

Timeseries

uncorrelated, binning

AverageType

autocorrelation

Timeseries

_distance, _limit

mctimeseries<AverageType>

exponential_autocorrelation_time

Scalar MCTimeseries

_from & _to, _max & _min

std::pair<AverageType, AverageType>

integrated_autocorrelation_time

Scalar MCTimeseries, std::pair<AverageType, AverageType>

None

AverageType

running_mean

Timeseries

None

mctimeseries<AverageType>

reverse_running_mean

Timeseries

None

mctimeseries<AverageType>

where AverageType is typename average_type<ValueType>::type,\\ Timeseries is one of mcdata<ValueType>, mctimeseries<ValueType> or mctimeseries_view<ValueType>[newline] and Scalar MCTimeseries` is one of mctimeseries<double> or mctimeseries_view<double>\

The objects mctimeseries<ValueType> and mctimeseries_view<ValueType> are essentially wrapped boost::shared_ptr's to the timeseries. while the constructor of the mctimeseries class copies the whole data, the constructor of the mctimeseries_view class only creates a reference. One can easily create views of timeseries by using the functions cut_head and cut_tail:

Function Name

Argument(s)

Options

Return Type

cut_head

Timeseries

_distance, _limit

mctimeseries_view<ValueType>

cut_tail

Timeseries

_distance, _limit

mctimeseries_view<ValueType>

Mean

Mean

Copyright 1994, 2002-2004, 2012 Matthias Troyer, Synge Todo, Maximilian Poprawe

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