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Probabilistic Graphical Models and Tensor Networks: A Hybrid Framework
[article]
2021
arXiv
pre-print
We investigate a correspondence between two formalisms for discrete probabilistic modeling: probabilistic graphical models (PGMs) and tensor networks (TNs), a powerful modeling framework for simulating complex quantum systems. The graphical calculus of PGMs and TNs exhibits many similarities, with discrete undirected graphical models (UGMs) being a special case of TNs. However, more general probabilistic TN models such as Born machines (BMs) employ complex-valued hidden states to produce novel
arXiv:2106.15666v1
fatcat:zamkpurkp5hfbk2ogd6pkwtrlu