For linear-rate master equations the generating function admits an exact composition-multiplier representation whose Taylor coefficients on any finite window are obtained from a closed lower-triangular ODE of size 2(N+1), independent of the truncation cap N; the same closure is combined with Strang–
year = 2008, month = may
5 Pith papers cite this work. Polarity classification is still indexing.
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Quantum algorithms achieve polylog(N) complexity for high-dimensional linear SDEs by amplitude-encoding the solution and noise via Dyson series or Euler-Maruyama approximations plus quantum linear systems solvers.
Markovian population models induce unique genealogy processes whose exact likelihoods are given by model-determined filter equations, generalizing prior phylodynamic methods.
M-CaStLe generalizes local stencil-based causal discovery to the multivariate case and decomposes resulting graphs into reaction and spatial components for interpretation in space-time gridded data.
Trajectory data resolves structural non-identifiability in lattice random walk diffusion models that count data alone cannot, with analysis of experimental design impacts on practical identifiability.
citing papers explorer
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Solving linear-rate ODE hierarchies (like master equations) using closures and operator splitting
For linear-rate master equations the generating function admits an exact composition-multiplier representation whose Taylor coefficients on any finite window are obtained from a closed lower-triangular ODE of size 2(N+1), independent of the truncation cap N; the same closure is combined with Strang–
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Quantum algorithm for solving high-dimensional linear stochastic differential equations via amplitude encoding of the noise term
Quantum algorithms achieve polylog(N) complexity for high-dimensional linear SDEs by amplitude-encoding the solution and noise via Dyson series or Euler-Maruyama approximations plus quantum linear systems solvers.
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Exact phylodynamic likelihood via structured Markov genealogy processes
Markovian population models induce unique genealogy processes whose exact likelihoods are given by model-determined filter equations, generalizing prior phylodynamic methods.
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M-CaStLe: Uncovering Local Causal Structures in Multivariate Space-Time Gridded Data
M-CaStLe generalizes local stencil-based causal discovery to the multivariate case and decomposes resulting graphs into reaction and spatial components for interpretation in space-time gridded data.
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When do trajectories matter? Identifiability analysis for stochastic transport phenomena
Trajectory data resolves structural non-identifiability in lattice random walk diffusion models that count data alone cannot, with analysis of experimental design impacts on practical identifiability.