  This work investigates application of different machine learning based
prediction methodologies to estimate the performance of silicon based textured
cells. Concept of confidence bound regions is introduced and advantages of this
concept are discussed in detail. Results show that reflection profiles and
depth dependent optical generation profiles can be accurately estimated using
Gaussian processes with exact knowledge of uncertainty in the prediction
values.It is also shown that cell design parameters can be estimated for a
desired performance metric.
