This module is a Bayesian model that uses terrain features and past human behavior data to predict the probability distribution of the likely places to find a missing person.

Internal Parameters:

• Candidate distributions means and standard deviations
• Burn (e.g. 1000)
• Number of iterations (e.g. 100000)
• Slope discretization theshold (e.g. s = 20 degrees)

Input Parameters:

• Search region (defined by GPS coordinates)
• Terrain features:
  • Topography (lake, hill, plane, etc.)
  • Vegetation density (Sparse, medium, dense)
  • Elevation (Uphill, flat, downhill)
  • Trail (On trail, off trail)
• Last point seen (GPS coordinates converting to hex coordinates)
• Intended destination (GPS coordinates converting to hex coordinates)
• Prior Beliefs (transitional probabilities ←- Get from ParamMod)
• Desired duration (time since missing person last seen in minute)

Output Parameters:

• Probability distribution maps at each minute (Array of matrices)

• Use CUDA to speed up matrix multiplications.
  • Play with CUDA Net Sample Code
  • CUDA Test App
• Interface to enter search region, last point seen, and (optionally) intended desitnation
• Tool to download/encode topography info (create look up table)
• Tool to label topography info (paint tool)
• Tool to download/encode vegetation density (create look up table)
• Tool to label trail info (paint tool)
• Allow user to specify last point seen and intended destination by clicking a map.
• Convert all GPS coordinates to hex coordinates.
• Extend model to support trail nodes
• Extend model to support intended destination

• Validate model correctness
  • Use n-fold cross validation with geocacher GPS track log data
  • Measure probability density for area within radius r of geocacher position
• Validate accuracy of representation
  • Use past real cases and have both expert and model generate probability distribution maps.
  • Ask expert to compare maps and reason the differences.
  • Identify significant differences (exceeding 50% for a certain region)
• Validate model usefulness
  • Use Charles Twardy's web site to evaluate models (prior and posterior).
  • Measure probability density for area within radius r of location where missing person was found.
  • Measure for multiple r values for sensitivity analysis.
  • Average densities across all cases to get general sense of how well the model is performing.

• Play with CUDA Sample Code
• Create CUDA Test App
• Implement CUDA in my app