   METAINFERENCE
 This is part of the isdb module

Calculates the Metainference energy for a set of experimental data.

Metainference  is a Bayesian framework to model heterogeneous systems by integrating prior information with noisy, ensemble-averaged data. Metainference models a system and quantifies the level of noise in the data by considering a set of replicas of the system.

Calculated experimental data are given in input as ARG while reference experimental values can be given either from fixed components of other actions using PARARG or as numbers using PARAMETERS. The default behavior is that of averaging the data over the available replicas, if this is not wanted the keyword NOENSEMBLE prevent this averaging.

Metadynamics Metainference  or more in general biased Metainference requires the knowledge of biasing potential in order to calculate the weighted average. In this case the value of the bias can be provided as the last argument in ARG and adding the keyword REWEIGHT. To avoid the noise resulting from the instantaneous value of the bias the weight of each replica can be averaged over a give time using the keyword AVERAGING.

The data can be averaged by using multiple replicas and weighted for a bias if present. The functional form of Metainference can be chosen among four variants selected with NOISE=GAUSS,MGAUSS,OUTLIERS,MOUTLIERS,GENERIC which correspond to modelling the noise for the arguments as a single gaussian common to all the data points, a gaussian per data point, a single long-tailed gaussian common to all the data points, a log-tailed gaussian per data point or using two distinct noises as for the most general formulation of Metainference. In this latter case the noise of the replica-averaging is gaussian (one per data point) and the noise for the comparison with the experimental data can chosen using the keyword LIKELIHOOD between gaussian or log-normal (one per data point), furthermore the evolution of the estimated average over an infinite number of replicas is driven by DFTILDE.

As for Metainference theory there are two sigma values: SIGMA_MEAN0 represent the error of calculating an average quantity 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. Alternatively, this can be obtained automatically using the internal sigma mean optimization as introduced in  (OPTSIGMAMEAN=SEM), in this second case sigma_mean is estimated from the maximum standard error of the mean either over the simulation or over a defined time using the keyword AVERAGING. 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. If the number of data point is too large and the acceptance rate drops it is possible to make the MC move over mutually exclusive, random subset of size MC_CHUNKSIZE and run more than one move setting MC_STEPS in such a way that MC_CHUNKSIZE*MC_STEPS will cover all the data points.

Calculated and experimental data can be compared modulo a scaling factor and/or an offset using SCALEDATA and/or ADDOFFSET, the sampling is obtained by a MC algorithm either using a flat or a gaussian prior setting it with SCALE_PRIOR or OFFSET_PRIOR.

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

Click on the labels of the actions for more information on what each action computes rdc: RDC ...

SCALEcompulsory keyword ( default=1. )
Add the scaling factor to take into account concentration and other effects. =0.0001
GYROMcompulsory keyword ( default=1. )
Add the product of the gyromagnetic constants for the bond. =-72.5388
ATOMS1the couple of atoms involved in each of the bonds for which you wish to calculate
the RDC. =22,23
ATOMS2the couple of atoms involved in each of the bonds for which you wish to calculate
the RDC. =25,27
ATOMS3the couple of atoms involved in each of the bonds for which you wish to calculate
the RDC. =29,31
ATOMS4the couple of atoms involved in each of the bonds for which you wish to calculate
the RDC. =33,34
...
spe: METAINFERENCE ...
ARGthe input for this action is the scalar output from one or more other actions. =rdc.*
NOISETYPEcompulsory keyword ( default=MGAUSS )
functional form of the noise (GAUSS,MGAUSS,OUTLIERS,MOUTLIERS,GENERIC) =MGAUSS
PARAMETERSreference values for the experimental data =1.9190,2.9190,3.9190,4.9190
SCALEDATA( default=off ) Set to TRUE if you want to sample a scaling factor common to all
values and replicas  SCALE0 could not find this keyword =1 SCALE_MINminimum value of the scaling factor =0.1 SCALE_MAXmaximum value of the scaling factor =3 DSCALEmaximum MC move of the scaling factor =0.01
SIGMA0 could not find this keyword =0.01 SIGMA_MINcompulsory keyword ( default=0.0 )
minimum value of the uncertainty parameter =0.00001 SIGMA_MAXcompulsory keyword ( default=10. )
maximum value of the uncertainty parameter =3 DSIGMAmaximum MC move of the uncertainty parameter =0.01
SIGMA_MEAN0 could not find this keyword =0.001

...
PRINT ARGthe input for this action is the scalar output from one or more other actions. =spe.bias FILEthe name of the file on which to output these quantities =BIAS STRIDEcompulsory keyword ( default=1 )
the frequency with which the quantities of interest should be output =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, furthermore the data are weighted for the bias applied to other variables of the system.

Click on the labels of the actions for more information on what each action computes rdc: RDC ...

SCALEcompulsory keyword ( default=1. )
Add the scaling factor to take into account concentration and other effects. =0.0001
GYROMcompulsory keyword ( default=1. )
Add the product of the gyromagnetic constants for the bond. =-72.5388
ATOMS1the couple of atoms involved in each of the bonds for which you wish to calculate
the RDC. =22,23
ATOMS2the couple of atoms involved in each of the bonds for which you wish to calculate
the RDC. =25,27
ATOMS3the couple of atoms involved in each of the bonds for which you wish to calculate
the RDC. =29,31
ATOMS4the couple of atoms involved in each of the bonds for which you wish to calculate
the RDC. =33,34
...
cv1: TORSION ATOMSthe four atoms involved in the torsional angle =1,2,3,4
cv2: TORSION ATOMSthe four atoms involved in the torsional angle =2,3,4,5
mm: METAD ARGthe input for this action is the scalar output from one or more other actions. =cv1,cv2 HEIGHTthe heights of the Gaussian hills. =0.5 SIGMAcompulsory keyword
the widths of the Gaussian hills =0.3,0.3 PACEcompulsory keyword
the frequency for hill addition =200 BIASFACTORuse well tempered metadynamics and use this bias factor. =8 WALKERS_MPI( default=off ) Switch on MPI version of multiple walkers - not compatible with WALKERS_*
options other than WALKERS_DIR
spe: METAINFERENCE ...
#SETTINGS NREPLICAS=2
ARGthe input for this action is the scalar output from one or more other actions. =rdc.*,mm.bias
REWEIGHT( default=off ) simple REWEIGHT using the latest ARG as energy
NOISETYPEcompulsory keyword ( default=MGAUSS )
functional form of the noise (GAUSS,MGAUSS,OUTLIERS,MOUTLIERS,GENERIC) =OUTLIERS
PARAMETERSreference values for the experimental data =1.9190,2.9190,3.9190,4.9190
SCALEDATA( default=off ) Set to TRUE if you want to sample a scaling factor common to all
values and replicas  SCALE0 could not find this keyword =1 SCALE_MINminimum value of the scaling factor =0.1 SCALE_MAXmaximum value of the scaling factor =3 DSCALEmaximum MC move of the scaling factor =0.01
SIGMA0 could not find this keyword =0.01 SIGMA_MINcompulsory keyword ( default=0.0 )
minimum value of the uncertainty parameter =0.00001 SIGMA_MAXcompulsory keyword ( default=10. )
maximum value of the uncertainty parameter =3 DSIGMAmaximum MC move of the uncertainty parameter =0.01
SIGMA_MEAN0 could not find this keyword =0.001

...