apfeffer
commented
7 years ago Hi Subho,
Unfortunately, our particle filter only proposes samples by the prior, so you do run into the rare event issue. As you suggest, there are particle filtering algorithms that use different proposals, but it’s hard to design such an algorithm that works in a general way with probabilistic programming. This would be an interesting research project.
For filtering with rare events, you might want to look at our factored frontier algorithm (a form of BP for filtering). This doesn’t have the rare event problem because it doesn’t depend on sampling, so if BP works well for your problem, it could be a good alternative.
Avi
From: Subho Banerjee notifications@github.com Reply-To: p2t2/figaro reply@reply.github.com Date: Wednesday, December 14, 2016 at 1:55 PM To: p2t2/figaro figaro@noreply.github.com Subject: [p2t2/figaro] Rare Event Handling in ParticleFilter Implementation (#650)
Consider the ValveReliabilityhttps://github.com/p2t2/figaro/blob/master/FigaroExamples/src/main/scala/com/cra/figaro/example/ValveReliability.scala example that is shipped along with Figaro. If any of the probabilities in f1, f2, or f3 were changed to so that there was several order of magnitude difference between the most likely and least likely event, ie.
Select(0.999999999 -> OK, 5E-10 -> RO, 5E-10-> RC)("f1", static)
then a sampling based filtering algorithm like the ParticleFilter will not be able to sample events that happen very rarely as they never get picked up and propagated. AFAIK the only way to get around this is by Importance sampling the particles to give a much higher weight to the less likely events.
As far as I understand, the standard Figaro ParticleFilter implements some form of the sequential importance sampling algorithm, but assigns weights only based on Observations/Conditions/Constraints (This also seems to be the case for the Importance sampler). Is there some way we can simulate rare event filtering/inference in Figaro?
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ssbanerje
Consider the
ValveReliabilityexample that is shipped along with Figaro. If any of the probabilities inf1,f2, orf3were changed to so that there was several order of magnitude difference between the most likely and least likely event, ie.then a sampling based filtering algorithm like the
ParticleFilterwill not be able to sample events that happen very rarely as they never get picked up and propagated. AFAIK the only way to get around this is by Importance sampling the particles to give a much higher weight to the less likely events.As far as I understand, the standard Figaro
ParticleFilterimplements some form of the sequential importance sampling algorithm, but assigns weights only based onObservations/Conditions/Constraints (This also seems to be the case for theImportancesampler). Is there some way we can simulate rare event filtering/inference in Figaro?