Statistics of spatio-temporal processes

September 11, 2020 — September 11, 2020

Gaussian
Hilbert space
kernel tricks
spatial
statistics
stochastic processes
time series

The dynamics of spatial processes evolving in time.

Figure 1

Clearly there are many different problems one might wonder about here. I am thinking in particular of the kind of problem whose discretisation might look like this, as a graphical model.

Figure 2

This is highly stylized - I’ve imagined there is one spatial dimension, but usually there would be two or three. The observed notes are where we have sensors that can measure the state of some parameter of interest \(w\) which evolves in time \(t\). I am wondering what we need to control for to simultaneously learn the parameters of the spatial field \(r_i\), the (possibly emulated) process \(p\) and the state of the unobserved \(w\) nodes.

1 Intros

Cosma Shalizi’s Data Over Space and Time course.

2 Ensemble Kalman Filters

A classic; happens to work pretty well with spatial fields. See Ensemble Kalman Filters.

3 Laplace approximation in spatial fields

AFAICT usually the justification we use for applying Gaussian process formalism to inference. See Laplace approximation for a background.

4 Low rank spatial fields

Fixed-Rank Kriging etc.

5 Tools

See python spatial and R spatial software.

6 References

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