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It's not your fault, these can get messy very quickly. Infer.NET was started because Tom Minka and other Bayes experts were tired of writing message passing and
by nextos 2y ago
It's not your fault, these can get messy very quickly. Infer.NET was started because Tom Minka and other Bayes experts were tired of writing message passing and variational inference by hand, which is both cumbersome and error prone on non-toy problems.
It helps to take a more abstract view where you split the generative process and the inference algorithm. Some frameworks (Infer.NET, ForneyLab.jl) can generate an efficient inference algorithm from the generative model without any user input. See e.g. https://github.com/biaslab/ForneyLab.jl/blob/master/demo/kalman_smoother_2d.ipynb https://github.com/biaslab/ForneyLab.jl/blob/master/demo/kal...
- ngriffiths 2y agoThanks for sharing this - saving this paper too (from link in the github page): https://people.ee.ethz.ch/~loeliger/localpapers/FactorGraphsProcIEEE2007.pdf https://people.ee.ethz.ch/~loeliger/localpapers/FactorGraphs... I'm not familiar with these techniques at all but seems like they have a ton of useful applications.
- nextos 2y agoFactor graphs are well discussed in David Barber's excellent free BRML book: http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/140324.pdf http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/140324.pdf Judea Pearl described it as an excellent Bayesian textbook. There's a free solutions book and everything is also implemented.