Confidence is not an applicable concept, although it is superficially similar. The question sounds rather like you want to identify the smallest region having a total probability at least 95%. This region can be obtained (at least conceptually) by sorting all the probabilities and accumulating them from highest to lowest until the partial sum first equals or exceed 95%, then selecting the cells corresponding to the values that have been accumulated. This leads to a straightforward solution, as exemplified by this R (open source) example:
set.seed(17) # Seed a reproducible random sequence
nr <- 30 # Number of rows
nc <- 50 # Number of columns
#
# Create a zone raster for normalizing the probabilities.
#
zone <- raster(ncol=nc, nrow=nr)
zone[] <- 0
#
# Create a probability raster (for illustrating the algorithm later).
#
p <- raster(ncol=nc, nrow=nr)
p[] <- (1:(nc*nr) - 1/2) / (nc*nr) + rnorm(nc*nr, sd=0.5)
p <- abs(focal(p, ngb=5, run=mean))
z <- zonal(p, zone, stat='sum')
p <- p / z[[2]] # This normalizes p to sum to unity as required
#------------------------------------------------------------------------------#
#
# The algorithm begins here.
#
pvec <- sort(getValues(p), decreasing=TRUE) # The probabilities, sorted
d <- cumsum(pvec) # Cumulative probabilities
dpos <- d[d <= 0.95] # Position to stop
region <- p # Initialize the output
region[p < pvec[length(dpos)]] <- NA # Exclude the last 5% of the probability
plot(region) # Display the result
Here is the resulting image of the 95% probability region with the original probabilities shown in color: they sum to just over 95%, by construction, and eliminating even the smallest value will reduce the sum to less than 95%. The white area at the top includes the remaining 5% of the probability outside this region. The desired contour is the boundary between the white cells and the colored cells.
The same method will work on a KDE grid.
There is no straightforward ArcGIS solution for this problem.
