"CcrossValidLik.MBparams.MBnetwork.MBdataDE.MBgibbsPars" <-
function(params,graph,data,gibbsPars,quiet=FALSE,fold=10,contrasts=NULL,...)
{
# Compatibility checks
    isCompat1 <- compatObjects(params,graph)
    isCompat2 <- compatObjects(graph,data)
    isValid <- validObject(gibbsPars)

    if(!is.numeric(fold) || (as.integer(fold) != fold) || (fold < 2) || (fold > data@N))
      stop("Invalid cross-validation fold number")

# Calculate training/test groups
    temp.sample <- sample(data@N)
    size <- round(data@N/fold)

# Analyze groups
    index <- 1
    liksum <- 0
    for(i in 1:fold) {
      sub <- rep(TRUE,data@N)
      if(i == fold) sub[temp.sample[index:data@N]] <- FALSE
      else {
        sub[temp.sample[index:(index+size-1)]] <- FALSE
        index <- index+size
      }
      if(!quiet) cat(paste("\nRunning gibbs sampler for separation ",i,"...\n\n",sep=""))
      graph <- calcCPDs(x=graph,data=data,contrasts)
      cpds.out <- graph@Cpds[,!sub]
      graph <- CgibbsSampler(x=graph,params=params,gibbsPars=gibbsPars,quiet=quiet,sub=sub,...)
      graph@Cpds[,!sub] <- NA
      graph <- CgibbsSampler(x=graph,params=params,gibbsPars=gibbsPars,quiet=quiet,prob=graph@Margs[2,],...)
      graph@Cpds[,!sub] <- cpds.out
      liksum <- liksum + sum(log(colSums(graph@Margs[,!sub]*graph@Cpds[,!sub])),na.rm=TRUE)
    }

    return(liksum)
}