Monte Carlo Methods¶
Monte Carlo methods use random sampling to solve problems that might be deterministic in principle.
Overview¶
In the context of filtering, Monte Carlo methods are primarily used for:
- Particle Filters - Represent the posterior distribution using a set of particles
- Resampling - Techniques for selecting particles based on their weights
- Importance Sampling - Drawing samples from a proposal distribution
Resampling Methods¶
The library provides several resampling algorithms:
- Multinomial Resampling
- Residual Resampling
- Stratified Resampling
- Systematic Resampling
API Reference¶
For detailed API documentation, see the Monte Carlo API reference.
Further Reading¶
For comprehensive examples and theory, see the companion book: Kalman and Bayesian Filters in Python