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A probabilistic programming language can make it much easier to implement a non-linear filter. Essentially, you implement the generative model, and you get infe

by nextos 3y ago

A probabilistic programming language can make it much easier to implement a non-linear filter. Essentially, you implement the generative model, and you get inference compiled for you. Interesting options to look at include ForneyLab.jl, Infer.net, Gen.jl, and Pyro.

tnecniv 3y ago
This can work if the time scale of inference is smaller than the time scale of your measurements. I’m skeptical that this would work on a quadrotor that requires a fast control loop. However, model predictive control (determining actions given a state and dynamic model; the Kalman filter is model predictive estimation) first found major use in chemical plants because they could crunch the numbers on a big computer that didn’t need to fit on a robot and the real time between each time step was large. For such a situation, you might be able to get MCMC to work.
- nextos 3y ago
  The options I suggested above are not necessarily MCMC, they are mostly message passing algorithms. ForneyLab and Gen in particular are designed for online inference.
  - tnecniv 3y ago
    Good point! Still, a big reason why the reason why the KF is a staple is that it’s really really fast. When I’ve used tools like the ones that you mentioned, it was normally not for inference not in a feedback loop. I’m less familiar with message passing methods than Monte Carlo methods but I’m going to look into them now
    - nextos 3y ago
      Message passing can lead to extremely fast maximum likelihood or variational inference. I have used it for realtime control problems. It is a very interesting domain!
      - tnecniv 3y ago
        Do you have a good reference? Preferably one that mentions a control application because I’m curious to see what the model assumptions and speed look like in that context
        nextos 3y ago
        You can start with a review [1], and then BRML [2]. I think applications that are very focused on control tend to come from the military and thus not so well documented. [1] An introduction to factor graphs. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1267047 https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1267047 [2] Bayesian Reasoning and Machine Learning. http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/200620.pdf http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/200620.pdf

by nextos 3y ago

tnecniv 3y ago

This can work if the time scale of inference is smaller than the time scale of your measurements. I’m skeptical that this would work on a quadrotor that requires a fast control loop. However, model predictive control (determining actions given a state and dynamic model; the Kalman filter is model predictive estimation) first found major use in chemical plants because they could crunch the numbers on a big computer that didn’t need to fit on a robot and the real time between each time step was large. For such a situation, you might be able to get MCMC to work.

nextos 3y ago
The options I suggested above are not necessarily MCMC, they are mostly message passing algorithms. ForneyLab and Gen in particular are designed for online inference.
- tnecniv 3y ago
  Good point! Still, a big reason why the reason why the KF is a staple is that it’s really really fast. When I’ve used tools like the ones that you mentioned, it was normally not for inference not in a feedback loop. I’m less familiar with message passing methods than Monte Carlo methods but I’m going to look into them now
  - nextos 3y ago
    Message passing can lead to extremely fast maximum likelihood or variational inference. I have used it for realtime control problems. It is a very interesting domain!
    - tnecniv 3y ago
      Do you have a good reference? Preferably one that mentions a control application because I’m curious to see what the model assumptions and speed look like in that context
      - nextos 3y ago
        You can start with a review [1], and then BRML [2]. I think applications that are very focused on control tend to come from the military and thus not so well documented. [1] An introduction to factor graphs. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1267047 https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1267047 [2] Bayesian Reasoning and Machine Learning. http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/200620.pdf http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/200620.pdf