The authors introduce Time to Transition (TtT) extracted from cross-maturity greenium differences and develop tractable deadline-constrained and regime-switching diffusion models with exact likelihoods and asymptotic identification results for inference.
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CAM is an unsupervised training method for discrete diffusion models on combinatorial optimization problems that uses discrete adjoint dynamics to supply low-variance trajectory-level signals.
A renormalization-group-inspired scale-splitting algorithm generates hierarchical formulas for dynamics in large dilute chemical reaction networks, illustrated on the formose reaction.
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Market-implied time to transition to a low-carbon economy: a stochastic modelling and inference framework
The authors introduce Time to Transition (TtT) extracted from cross-maturity greenium differences and develop tractable deadline-constrained and regime-switching diffusion models with exact likelihoods and asymptotic identification results for inference.
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Unsupervised Diffusion Solver for Combinatorial Optimization via Combinatorial Adjoint Matching
CAM is an unsupervised training method for discrete diffusion models on combinatorial optimization problems that uses discrete adjoint dynamics to supply low-variance trajectory-level signals.
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Hierarchical models for large chemical reaction networks
A renormalization-group-inspired scale-splitting algorithm generates hierarchical formulas for dynamics in large dilute chemical reaction networks, illustrated on the formose reaction.