A projected process kriging algorithm for sensor networks with heterogeneous error characteristics

Keyphrases

• Sensor Networks 100%
• Error Characteristics 100%
• Kriging Algorithm 100%
• Non-Gaussian 50%
• Sensor Model 50%
• Geostatistics 50%
• Monitoring Network 50%
• Computationally Intensive 25%
• Gaussian Process Prior 25%
• Posterior Distribution 25%
• Bayesian Inference Method 25%
• Real-world Application 25%
• High Temporal Resolution 25%
• Kriging 25%
• Monte Carlo Method 25%
• Sensor Failure 25%
• Gaussian Errors 25%
• Model Characteristics 25%
• Model-based Geostatistics 25%
• Approximate Bayesian Inference 25%
• Simulated Dataset 25%
• Latent Process 25%
• Coherent Map 25%
• Sensor Type 25%
• Model Form 25%

Mathematics

• Kriging 100%
• Approximates 50%
• Gaussian Distribution 50%
• Inference Method 50%
• Gaussian Process 50%
• Monte Carlo 50%
• Posterior Distribution 50%
• Bayesian Inference 50%
• Model Form 50%

Engineering

• Sensor Network 100%
• Error Characteristic 100%
• Gaussians 50%
• Sensor Model 50%
• Prior Process 25%
• Posterior Distribution 25%
• Real World Application 25%
• Temporal Resolution 25%
• Form Part 25%
• Model Form 25%