Some mathematical tutorials and notes relevant to robotics, including but not limited to measure theory, real analysis, probabilistic theory, stochastic analysis, Lie theory, topology, differential geometry, differential manifolds, optimization on Riemannian manifolds.

• Advanced Probability Theory (Renlang Huang)

• Advanced Probability Theory
• Probability Theory and Examples
• An Introduction to Stochastic Differential Equations

• A micro Lie theory for state estimation in robotics
• MCMC using Hamiltonian dynamics
• Geodesic Monte Carlo on Embedded Manifolds
• Bayesian Learning via Stochastic Gradient Langevin Dynamics (ICML 2011)
• Stochastic Gradient Hamiltonian Monte Carlo (ICML 2014)
• Bayesian Sampling Using Stochastic Gradient Thermostats (NIPS 2014)
• On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators (NIPS 2015)
• Stochastic Gradient Geodesic MCMC Methods (NIPS 2016)
• Bayesian Pose Graph Optimization via Bingham Distributions and Tempered Geodesic MCMC (NeurIPS 2018)
• Distributed Certifiably Correct Pose-Graph Optimization (T-RO 2021)
• Stein Particle Filter for Nonlinear, Non-Gaussian State Estimation (RA-L 2022)