I coded up a toy problem from the (paywalled) Sigworth paper and solved it by optimizing a marginalized likelihood function. The toy problem is an 8×8 image subject to all possible 64 integer cyclic shifts and 4 integer rotations (256 possible views) and then noisified. The shifts and rotations are not known in advance; we only have the data. The marginalized likelihood optimization rocks! Below find 16 data examples (from a set of 512 total) and then some iterations (starting from a random guess) of the likelihood optimization. Rocking it!
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