State Space Models: A Modern Approach

State Space Models: A Modern Approach

Scratchpad
State Space Models
Hidden Markov Models
Linear-Gaussian SSMs
Extended (linearized) methods
Unscented methods
- Unscented filtering
- Unscented smoothing
Quadrature and cubature methods
Posterior linearization
Assumed Density Filtering
Variational inference
Particle filtering
Sequential Monte Carlo
Offline parameter estimation (learning)
Multi-target tracking
Data assimilation using Ensemble Kalman filter
Bayesian non-parametric SSMs
Changepoint detection
Timeseries forecasting
Markovian Gaussian processes
Differential equations and SSMs
Optimal control
Bibliography

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State Space Models

State Space Models¶

This chapter introduces state space models (SSMs).

What are State Space Models?
Hidden Markov Models
Linear Gaussian SSMs
Nonlinear Gaussian SSMs
Inferential goals

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What are State Space Models?

By Kevin Murphy, Scott Linderman, et al.
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