State Space Models: A Modern Approach

State Space Models: A Modern Approach

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
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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
States estimation (inference)
Parameter estimation (learning)

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State Space Models: A Modern Approach

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By Kevin Murphy, Scott Linderman, et al.
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