Models composed from bilinear factor, exponential link, Gamma prior, Gaussian likelihood, and equality node admit closed-form variational message passing under mean-field factorization.
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CppSim delivers GPU-accelerated tensor network dynamics for the 2D Ising model via zero-malloc workspace, custom permutation kernel (7.6x Trotter speedup), hybrid QR, and adaptive log-space belief propagation.
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Composing Non-Conjugate Factor Graphs with Closed-Form Variational Inference
Models composed from bilinear factor, exponential link, Gamma prior, Gaussian likelihood, and equality node admit closed-form variational message passing under mean-field factorization.
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GPU-First Heisenberg-Picture Tensor Network Dynamics for the 2D Transverse-Field Ising Model
CppSim delivers GPU-accelerated tensor network dynamics for the 2D Ising model via zero-malloc workspace, custom permutation kernel (7.6x Trotter speedup), hybrid QR, and adaptive log-space belief propagation.