Are there any libraries for discrete probability tables in order to do these type of operations directly (see images)? I’m looking for having discrete probability tables with these operations defined on them. I also want them to be sparse (see last image). I.e., I want zero probabilities (or sometimes even an arbitrary common-to-many-states probability) to have no “entry”/ take up no memory. Is there anything that already fit these requirements? Or are there perhaps some legwork that I can pick up and enhance? [image (0)] [image (1)] [image (2)] [image (3)] [image (4)] [image (5)] [image (6)] Note that the original poster on Slack cannot see your response here on Discourse. Consider transcribing the appropriate answer back to Slack, or pinging the poster here on Discourse so they can follow this thread. (Original message :slack:) (More Info)

[image] Boston Gelwan 18 hours ago Distributions.jl has discrete distributions, but these operations are best represented by linear algebra. LinearAlgebra should suffice entirely. (edited) [image] Heetbeet 16 hours ago So I was thinking the same some years ago and got frustrated with matching/reshuffling dimensions to fit the “variable” labels. E.g. p(x,y,z) == p(y,x,z), and p(x,y) may be multiplied with p(y,z) with only one variable in the overlapping scope. I wanted a sparse implementation, and I’m not sure how the memory mapping reshaping arrays worked back in the day in Python. In C++ I have an object that with the main data structure similar to Dict{Vector{Int}, Any} to represent this: (edited) image (6).png [image (6).png] [image] Heetbeet 16 hours ago Along with some other helper data such as the A,B,C,D labels. These are wrapped in a object DiscreteTable which is a subclass of Factor Those operations were then defined on DiscreteTable and allowed DiscreteTable to operate in other algorithms designed for Factor classes. But doing anything in that library is such a hassle and a big mess. I’m busy writing up my thesis, and I was wondering if it’s not better to just get the minimal stuff I did working in Julia and then be able to write my experiments much faster. [image] Heetbeet 16 hours ago So I can obviously go with the same structure as C++ in Julia, but I have the same hunch as you @extradosages: LinearAlgebra should be able to reach this problem. I just don’t have any idea how to do the dimension->variable mapping, and have everything sparse. (edited) [image] Heetbeet 16 hours ago Hmm, this might help me: Implementing an equivalent of python's xarray (labeled nd-arrays) in julia (edited) [image] Heetbeet 16 hours ago I might be wrong, but it seems like GitHub - JuliaArrays/AxisArrays.jl: Performant arrays where each dimension can have a named axis with values annotates the “indexes”, i.e. for a 2x2 array, each row can get a name and each column can get a name. But what I would need is to let each dimension have a name, like :row and :column and not each item within that dimension. [image] Heetbeet 16 hours ago This looks exactly like what I want: [image] GitHub - davidavdav/NamedArrays.jl: Julia type that implements a drop-in... Julia type that implements a drop-in replacement of Array with named dimensions - GitHub - davidavdav/NamedArrays.jl: Julia type that implements a drop-in replacement of Array with named dimensions

Are there any libraries for discrete probability tables in order to do these type

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heetbeet March 31, 2021, 2:53pm 3

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Are there any libraries for discrete probability tables in order to do these type

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