METAINFERENCE
 This is part of the bias module

Calculate the Metainference Score for a set of back calculated experimental data.

The back calculated data, that are expected to be averages over replicas (NR=1,2,..,N) The functional form of this bias can be chosen between three variants selected with NOISE=GAUSS,MGAUSS,OUTLIERS which correspond to modelling the noise for the arguments as a single gaussian common to all the data points, a gaussian per data point or a single long-tailed gaussian common to all the data points.

As from Metainference theory there are two sigma values: SIGMA_MEAN represent the error of calculating an average quanity using a finite set of replica and should be set as small as possible following the guidelines for replica-averaged simulations in the framework of the Maximum Entropy Principle. SIGMA_BIAS is an uncertainty parameter, sampled by a MC algorithm in the bounded interval defined by SIGMA_MIN and SIGMA_MAX. The initial value is set at SIGMA0. The MC move is a random displacement of maximum value equal to DSIGMA.

Description of components

The names of the components in this action can be customized by the user in the actions input file. However, in addition to these customizable components the following quantities will always be output

 Quantity Description bias the instantaneous value of the bias potential sigma uncertainty parameter scale scale parameter accept MC acceptance
Compulsory keywords
 NOISETYPE functional form of the noise (GAUSS,MGAUSS,OUTLIERS) SCALE0 initial value of the uncertainty parameter SCALE_MIN minimum value of the uncertainty parameter SCALE_MAX maximum value of the uncertainty parameter DSCALE maximum MC move of the uncertainty parameter SIGMA0 initial value of the uncertainty parameter SIGMA_MIN minimum value of the uncertainty parameter SIGMA_MAX maximum value of the uncertainty parameter DSIGMA maximum MC move of the uncertainty parameter SIGMA_MEAN starting value for the uncertainty in the mean estimate
Options
 NUMERICAL_DERIVATIVES ( default=off ) calculate the derivatives for these quantities numerically SCALEDATA ( default=off ) Set to TRUE if you want to sample a scaling factor common to all values and replicas ARG the input for this action is the scalar output from one or more other actions. The particular scalars that you will use are referenced using the label of the action. If the label appears on its own then it is assumed that the Action calculates a single scalar value. The value of this scalar is thus used as the input to this new action. If * or *.* appears the scalars calculated by all the proceding actions in the input file are taken. Some actions have multi-component outputs and each component of the output has a specific label. For example a DISTANCE action labelled dist may have three componets x, y and z. To take just the x component you should use dist.x, if you wish to take all three components then use dist.*.More information on the referencing of Actions can be found in the section of the manual on the PLUMED Getting started. Scalar values can also be referenced using POSIX regular expressions as detailed in the section on Regular Expressions. To use this feature you you must compile PLUMED with the appropriate flag. You can use multiple instances of this keyword i.e. ARG1, ARG2, ARG3... PARARG reference values for the experimental data, these can be provided as arguments without derivatives PARAMETERS reference values for the experimental data TEMP the system temperature - this is only needed if code doesnt' pass the temperature to plumed MC_STEPS number of MC steps MC_STRIDE MC stride
Examples

In the following example we calculate a set of RDC, take the replica-average of them and comparing them with a set of experimental values. RDCs are compared with the experimental data but for a multiplication factor SCALE that is also sampled by MC on-the-fly

RDC ...
LABEL=rdc
SCALE=0.0001
GYROM=-72.5388
ATOMS1=22,23
ATOMS2=25,27
ATOMS3=29,31
ATOMS4=33,34
... RDC

ardc: ENSEMBLE ARG=rdc.*

METAINFERENCE ...
ARG=ardc.*
NOISETYPE=MGAUSS
PARAMETERS=1.9190,2.9190,3.9190,4.9190
SCALEDATA SCALE0=1 SCALE_MIN=0.00001 SCALE_MAX=3 DSCALE=0.00
SIGMA0=0.01 SIGMA_MIN=0.00001 SIGMA_MAX=3 DSIGMA=0.00
SIGMA_MEAN=0.001
TEMP=300
LABEL=spe
... METAINFERENCE

PRINT ARG=spe.bias FILE=BIAS STRIDE=1


in the following example instead of using one uncertainty parameter per data point we use a single uncertainty value in a long-tailed gaussian to take into account for outliers.

METAINFERENCE ...
ARG=ardc.*
NOISETYPE=OUTLIERS
PARAMETERS=1.9190,2.9190,3.9190,4.9190
SCALEDATA SCALE0=1 SCALE_MIN=0.00001 SCALE_MAX=3 DSCALE=0.00
SIGMA0=0.01 SIGMA_MIN=0.00001 SIGMA_MAX=3 DSIGMA=0.00
SIGMA_MEAN=0.001
TEMP=300
LABEL=spe
... METAINFERENCE