Tutorial Notebooks for Bayesian Filtering in Linear and Nonlinear State-Space Models #292

schmidtjonathan · 2020-12-22T18:12:48Z

Closes #237

review-notebook-app · 2020-12-22T18:12:52Z

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pnkraemer · 2021-02-17T07:46:27Z

docs/source/tutorials/filtsmooth/linear_bayesian_filtering_smoothing.ipynb

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Applied Stochastic Differential Equations
Bayesian Filtering and Smoothing

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pnkraemer · 2021-02-17T07:46:27Z

docs/source/tutorials/filtsmooth/linear_bayesian_filtering_smoothing.ipynb

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the align block somehow does not render well -- at least in ReviewNB.

also: "Note that this can be generalized to a linear, time-varying state space model"

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pnkraemer · 2021-02-17T07:46:27Z

docs/source/tutorials/filtsmooth/linear_bayesian_filtering_smoothing.ipynb

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I dont think the inline comments here are necessary -- it is just two lines with descriptive variable names

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pnkraemer · 2021-02-17T07:46:27Z

docs/source/tutorials/filtsmooth/linear_bayesian_filtering_smoothing.ipynb

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Perhaps add some explanation aka "This example thus requires a discrete, lti, Gaussian transition. In Probnum, this is the "DiscreteLTIGaussian" object, which needs to be initialised with the state transition, shift vector and process noise covariance matrix."

What do you think? I suppose this would apply to the blocks below, too.

It is also fine though if you dont wanna do it.

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pnkraemer · 2021-02-17T07:46:28Z

docs/source/tutorials/filtsmooth/linear_bayesian_filtering_smoothing.ipynb

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linear, time-invariant

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For the sake of consistency, I would prefer that to stay capitalized, if that's ok with you.

I did not mean capitalisation, but the comma and the dash: Linear, Time-Invariant

💡 Alright :)

pnkraemer · 2021-02-17T07:46:28Z

docs/source/tutorials/filtsmooth/linear_bayesian_filtering_smoothing.ipynb

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see my "comments" comment above

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pnkraemer · 2021-02-17T07:46:28Z

docs/source/tutorials/filtsmooth/linear_bayesian_filtering_smoothing.ipynb

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I'd say here we would need some explanation of what is going on? I.e. statespace.generate samples from the state space model.

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pnkraemer · 2021-02-17T07:46:28Z

docs/source/tutorials/filtsmooth/linear_bayesian_filtering_smoothing.ipynb

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sampling and interpolation? Not prediction?

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pnkraemer · 2021-02-17T07:46:28Z

docs/source/tutorials/filtsmooth/linear_bayesian_filtering_smoothing.ipynb

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have you tried plotting the uncertainty sausages in the same plot as the measurents? Would be good to see if their width is appropriate or not

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They _are_plotted in every plot. For the components that are observed, the uncertainty is just lower (above row of plots)

ah okay my bad, I did not see that.

pnkraemer · 2021-02-17T07:46:28Z

docs/source/tutorials/filtsmooth/linear_bayesian_filtering_smoothing.ipynb

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"Only the dynamics model changes' -- well, the data does too?

"linear, time-invariant stochastic differential equation (SDE)".

Please remind me that we should discuss whether we want to talk about "Wiener processes", or "Brownian motion", and which letters we use in the notation.

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I think talking about Brownian Motion might be more consistent, cf. IBM. What do you think?

in this case yes. Though, on a side note, I personally prefer Wiener process (as this is also how I write my papers about this stuff, and AFAIK, nathanael does too), but this is beyond this PR. There is a discussion in the ODE notation about this issue, so we can continue there,.

review-notebook-app · 2021-02-17T07:46:30Z

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pnkraemer commented on 2021-02-17T07:46:30Z
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maths doesnt render well

schmidtjonathan commented on 2021-02-23T10:23:49Z
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That's an issue with ReviewNB, I think. Because in the notebook, it does.

pnkraemer commented on 2021-02-23T10:49:13Z
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this is what I get on readthedocs. I dont think the "$" should be there

schmidtjonathan commented on 2021-02-23T10:56:11Z
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Oh damn, my bad! Fixed.

Thanks for catching that!

review-notebook-app · 2021-02-17T07:46:30Z

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pnkraemer commented on 2021-02-17T07:46:30Z
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sounds a bit salty ;)

schmidtjonathan commented on 2021-02-23T10:24:25Z
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Does it? :D Not intended

pnkraemer commented on 2021-02-23T10:50:20Z
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hmmmm now that I read it again, I am less sure about my comment... maybe I was in a petty mood last time, feel free to ignore this :D

schmidtjonathan commented on 2021-02-23T10:57:20Z
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Alright :D

review-notebook-app · 2021-02-17T07:46:31Z

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pnkraemer commented on 2021-02-17T07:46:31Z
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maybe a side remark as to what happens with less easy-to-derive jacobians? I.e. use UKF or AD framework of your choice.

review-notebook-app · 2021-02-17T07:46:31Z

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pnkraemer commented on 2021-02-17T07:46:31Z
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samplign and interpolation?

review-notebook-app · 2021-02-17T07:46:32Z

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pnkraemer commented on 2021-02-17T07:46:32Z
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maybe label with "95%" stdev, instead of 1.96?

schmidtjonathan commented on 2021-02-23T10:34:07Z
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I prefer the version as is, but I'm ok with changing it, if you wish :)

pnkraemer commented on 2021-02-23T10:51:23Z
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I think it is personal preference, so your call. I personally prefer the percentage, because it is easier to parse, but I guess one can also argue for the 1.96 thing to be more representative of what is going on. If you prefer this, go ahead with it

pnkraemer · 2021-02-17T07:50:11Z

Overall, it looks great! Thanks for your efforts on this!

Most of my comments are small things. I only commented some things once, whereas it could be said about the entire PR. Do with this information what you like :)

schmidtjonathan · 2021-02-23T10:23:49Z

That's an issue with ReviewNB, I think. Because in the notebook, it does.

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schmidtjonathan · 2021-02-23T10:24:26Z

Does it? :D Not intended

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schmidtjonathan · 2021-02-23T10:34:08Z

I prefer the version as is, but I'm ok with changing it, if you wish :)

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pnkraemer · 2021-02-23T10:49:14Z

this is what I get on readthedocs. I dont think the "$" should be there

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pnkraemer · 2021-02-23T10:50:21Z

hmmmm now that I read it again, I am less sure about my comment... maybe I was in a petty mood last time, feel free to ignore this :D

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pnkraemer · 2021-02-23T10:51:24Z

I think it is personal preference, so your call. I personally prefer the percentage, because it is easier to parse, but I guess one can also argue for the 1.96 thing to be more representative of what is going on. If you prefer this, go ahead with it

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schmidtjonathan · 2021-02-23T10:56:12Z

Oh damn, my bad! Fixed.

Thanks for catching that!

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schmidtjonathan · 2021-02-23T10:57:21Z

Alright :D

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schmidtjonathan · 2021-02-23T11:32:50Z

@pnkraemer
Concerning the align environments:
Removing the $ caused tox to throw warnings, so I added $$ instead, and now it seems to work. Would you mind checking if it's ok?

pnkraemer

Looks good, thanks for your work on this!

schmidtjonathan added documentation Improvements or additions to documentation filtsmooth Issues related to filtering and smoothing labels Dec 22, 2020

schmidtjonathan self-assigned this Dec 22, 2020

schmidtjonathan force-pushed the filtering_tutorials branch from 1ce9abc to a73a1be Compare February 16, 2021 14:04

schmidtjonathan requested a review from pnkraemer February 16, 2021 15:47

schmidtjonathan force-pushed the filtering_tutorials branch from e7e3b24 to 4223bbb Compare February 16, 2021 16:12

pnkraemer reviewed Feb 17, 2021

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pnkraemer mentioned this pull request Feb 19, 2021

ODE notation #285

Closed

schmidtjonathan force-pushed the filtering_tutorials branch from 42c2f63 to b42a0d2 Compare February 23, 2021 10:39

schmidtjonathan requested a review from pnkraemer February 23, 2021 13:56

schmidtjonathan force-pushed the filtering_tutorials branch from e5e42dc to 7668275 Compare February 24, 2021 08:11

pnkraemer approved these changes Feb 24, 2021

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schmidtjonathan added 2 commits February 24, 2021 15:19

First version of filtering tutorial notebook

8f36540

Added pendulum example to tutorial notebook

ed85d53

schmidtjonathan added 15 commits February 24, 2021 15:19

Added Benes-Daum Filter example and some descriptions

790f873

Split up tutorials; added descriptions

702c2f5

Preliminarily final version of linear filter tutorial

b837f97

Improved descriptions in nonlinear filtering tutorial

ce259fc

Adds .rst file

f3a7498

Updated index.rst

9e52245

Updated index.rst

80b9b7a

Update index.rst

24e10cd

Adapted jupyter notebooks to changes in interface

cfcb4b8

Resolved comments in linear notebook

370a329

Resolved comments in non-linear notebook

3e91a5f

Adapted to changes in interface

d08f161

Fixed some formatting issues

3619cf9

Fixed warnings caused by math environments

4e1324c

Fixed some more formatting issues

eda6a41

schmidtjonathan force-pushed the filtering_tutorials branch from 7668275 to eda6a41 Compare February 24, 2021 14:19

schmidtjonathan merged commit 17b3874 into probabilistic-numerics:master Feb 24, 2021

Tutorial Notebooks for Bayesian Filtering in Linear and Nonlinear State-Space Models #292

Tutorial Notebooks for Bayesian Filtering in Linear and Nonlinear State-Space Models #292

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