Group Importance Sampling for particle filtering and MCMC

Mathematics

• Particle Filtering 90%
• Importance Sampling 66%
• Markov Chain Monte Carlo 59%
• Monte Carlo Techniques 56%
• Vegetation 18%
• Wireless Sensor Networks 17%
• Particle Filter 16%
• Metropolis-Hastings 15%
• Metropolis-Hastings Algorithm 15%
• Hyperparameters 14%
• Markov Chain Monte Carlo Algorithms 13%
• Synthetic Data 13%
• Sampling Methods 13%
• Resampling 12%
• Bayesian Methods 12%
• Signal Processing 12%
• MATLAB 12%
• Posterior distribution 11%
• Gaussian Process 11%
• Benchmark 11%
• Learning 10%
• Markov chain 9%
• Trajectory 9%
• Numerical Experiment 8%
• Observation 7%
• Estimator 6%
• Framework 6%
• Standards 6%

Business & Economics

• Markov Chain Monte Carlo 100%
• Particle Filtering 94%
• Importance Sampling 78%
• Metropolis-Hastings Algorithm 19%
• Wireless Sensor Networks 18%
• Particle Filter 17%
• Vegetation 17%
• Gaussian Process 16%
• Resampling 15%
• Posterior Distribution 14%
• Bayesian Methods 14%
• Localization 12%
• Sampling Methods 12%
• Markov Chain 12%
• Numerical Experiment 11%
• Trajectory 10%
• Integral 9%
• Estimator 9%
• Acceptance 9%
• Benchmark 8%

Engineering & Materials Science

• Importance sampling 74%
• Markov processes 53%
• Signal processing 9%
• Wireless sensor networks 8%
• Satellites 8%
• Sampling 8%
• Trajectories 8%
• Set theory 7%