Bayes prediction of unknown data values

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Bayes prediction of unknown data values

I am creating a tree inventory for a city.  I have measured variables such as height and recorded the species of about 2000 trees in different land types within the city.  

I have heights and locations for all the unmeasured trees which I obtained from Lidar.  I want to assign species to these unmeasured trees based on their location (landtype e.g. city centre, suburb) and height.  Landtype is therefore a category and height a continuous variable.  

Rather than just calculating the average species composition within the landtypes and the average heights for each species and using this to estimate the unmeasured trees, I would like to use a bayesian method.

Can anyone point me in the right direction of how to do this?