Kalman-and-Bayesian-Filters-in-Python VS paip-lisp

A fantastic interactive introduction to Kalman filters can be found on the following repo:

https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Pyt...

It explains them from first principles and provides the intuitive rationale for them but doesn't shy away from the math when it feels the student should be ready for it.

No thread on Kalman Filters is complete without a link to this excellent learning resource, a book written as a set of Jupyter notebooks:

https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Pyt...

That book mentions alpha-beta filters as sort of a younger sibling to full-blown Kalman filters. I recently had need of something like this at work, and started doing a bunch of reading. Eventually I realized that alpha-beta filters (and the whole Kalman family) is very focused on predicting the near future, whereas what I really needed was just a way to smooth historical data.

So I started reading in that direction, came across "double exponential smoothing" which seemed perfect for my use-case, and as I went into it I realized... it's just the alpha-beta filter again, but now with different names for all the variables :(

I can't help feeling like this entire neighborhood of math rests on a few common fundamental theories, but because different disciplines arrived at the same systems via different approaches, they end up sounding a little different and the commonality is obscured. Something about power series, Euler's number, gradient descent, filters, feedback systems, general system theory... it feels to me like there's a relatively small kernel of intuitive understanding at the heart of all that stuff, which could end up making glorious sense of a lot of mathematics if I could only grasp it.

Somebody help me out, here!

This provides an excellent intro that jumps right into code. https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python

If you know a bit of Python and you find it sometimes tough to grind through a textbook, take a look here:

https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Pyt...

Interactive examples programmed in Jupyter notebooks.

Thank you, very good resource! Timely too, as I am revising this topic.

My work is mostly in python. I found this interactive book using Jupyter that explains Kalman filters from first principles.

https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Pyt...

Try this book

https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python And on robotics in general

- Kalman and Bayesian Filters in Python

Have you seen https://stevelosh.com/blog/2018/08/a-road-to-common-lisp/ ? "Kludges" everywhere is applicable. On the other hand, having a function like "row-major-aref" that allows accessing any multi-dimensional array as if it were one dimensional is "sweeter than the honeycomb".

I still think CL code can be beautiful. Norvig's in PAIP https://github.com/norvig/paip-lisp is nice.

As for the inside-out remark, while technically you do it, you don't have to, and it's very convenient to not do. Clojure has its semi-famous arrow macro that lets you write things in a more sequential style, it exists in CL too, and there's always the venerable let* binding. e.g. 3 options:

    (loop (print (eval (read))))

PAIP by Peter Norvig, Chapter 23, Compiling Lisp

https://github.com/norvig/paip-lisp/blob/main/docs/chapter23...

Emacs is so much more than a text editor! But I need to stay on topic...

I believe your assessment of LISP (and therefore of MacArthy)'s impact on AI to be unfair. Just a few days ago https://github.com/norvig/paip-lisp was discussed on this site, for example.

Sounds like a great project idea to make a toy demo of this direction you'd like to see. Maybe comparable to https://github.com/norvig/paip-lisp/blob/main/docs/chapter15... and https://github.com/norvig/paip-lisp/blob/main/docs/chapter8.... which are a few hundred lines of Lisp each, but do enough to be interesting.

You could look into Paradigms of AI Programming by Peter Norvig which might interest you regardless of Lisp content.

Lisp has great power, examine PAIP, part II chapters 7 and 8.

A computer running Prolog reasons, and that only requires a couple of pages of code. So it seems feasible that the network could have learned some ability to reason within its network.

IMHO it's because lisp shines to manipulate symbols whereas the current AI trend is crunching matrices.

When AI was about building grammars, trees, developing expert systems builds rules etc. symbol manipulation was king. Look at PAIP for some examples: https://github.com/norvig/paip-lisp

This paradigm has changed.

Origen: Conversación con Bing, 4/4/2023(1) gigamonkey/monkeylib-binary-data - GitHub. https://github.com/gigamonkey/monkeylib-binary-data Con acceso 4/4/2023. (2) paip-lisp/chapter4.md at main · norvig/paip-lisp · GitHub. https://github.com/norvig/paip-lisp/blob/main/docs/chapter4.md Con acceso 4/4/2023. (3) bibliography.md · GitHub. https://gist.github.com/gigamonkey/6151820 Con acceso 4/4/2023.