  We present a statistical algorithm for predicting the [CII] emission from
Herschel and Spitzer continuum images using probability density functions
between the [CII] emission and continuum emission. The [CII] emission at 158
$\mu$m is a critical tracer in studying the life cycle of interstellar medium
and galaxy evolution. Unfortunately, its frequency is in the far infrared
(FIR), which is opaque through the troposphere and cannot be observed from the
ground except for highly red-shifted sources (z $\gtrsim$ 2). Typically [CII]
observations of closer regions have been carried out using suborbital or space
observatories. Given the high cost of these facilities and limited time
availability, it is important to have highly efficient observations/operations
in terms of maximizing science returns. This requires accurate prediction of
the strength of emission lines and, therefore, the time required for their
observation. However, [CII] emission has been hard to predict due to a lack of
strong correlations with other observables. Here we adopt a new approach to
making accurate predictions of [CII] emission by relating this emission
simultaneously to several tracers of dust emission in the same region. This is
done using a statistical methodology utilizing probability density functions
(PDFs) among [CII] emission and Spitzer IRAC and Herschel PACS/SPIRE images.
Our test result toward a star-forming region, RCW 120, demonstrates that our
methodology delivers high-quality predictions with less than 30\% uncertainties
over 80\% of the entire observation area, which is more than sufficient to test
observation feasibility and maximize science return. The {\it pickle} dump
files storing the PDFs and trained neural network module are accessible upon
request and will support future far-infrared missions, for example, GUSTO and
FIR Probe.
