Inference of Stochastic Disease Transmission Models Using Particle-MCMC and a Gradient Based Proposal

Keyphrases

• Disease Transmission 100%
• No-U-Turn Sampler 100%
• Metropolis-Hastings 100%
• Particle MCMC 100%
• Gradient-based 100%
• Relevant Parameters 50%
• Disease Rate 50%
• Communicable Diseases 50%
• Recovery Rate 50%
• Time Series Data 50%
• Multiple Parameters 50%
• Bayesian Parameter Estimation 50%
• SIR Model 50%
• Susceptible-infected-recovered Model 50%
• Population Activity 50%
• One Target 50%
• Stochasticity 50%
• Full Bayesian 50%
• Reasonable Time 50%
• Particle Filter 50%
• State-space Model 50%
• Stationary Distribution 50%
• True Nature 50%
• Stochastic Nonlinear 50%
• Random Walk 50%
• Target Evaluation 50%
• Gradient Information 50%

Mathematics

• Transmission Model 100%
• Stochastics 100%
• Bayesian 50%
• Parameter Estimation 50%
• Nonlinear 50%
• Time Series Data 50%
• SIR Model 50%
• Space Model 50%
• Random Walk 50%

Social Sciences

• Diseases 100%
• Stochastics 100%
• Time Series 50%
• Infectious Disease 50%
• State Space Model 50%