Hyperiax supplies JAX-based tree algorithms that make BFFG feasible for parameter inference and ancestral state reconstruction from landmark shapes on phylogenies with hundreds of tips.
Stochastics of shapes and Kunita flows
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Stochastic processes of evolving shapes are used in applications including evolutionary biology, where morphology changes stochastically as a function of evolutionary processes. Due to the non-linear and often infinite-dimensional nature of shape spaces, the mathematical construction of suitable stochastic shape processes is far from immediate. We define and formalize properties that stochastic shape processes should ideally satisfy to be compatible with the shape structure, and we link this to Kunita flows that, when acting on shape spaces, induce stochastic processes that satisfy these criteria by their construction. We couple this with a survey of other relevant shape stochastic processes and show how bridge sampling techniques can be used to condition shape stochastic processes on observed data thereby allowing for statistical inference of parameters of the stochastic dynamics.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
NBFFG combines a closed-form backward filter from a linear-Gaussian proxy process with a learned neural residual to enable efficient variational inference and unbiased pathwise subsampling for nonlinear diffusions on trees.
citing papers explorer
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Hyperiax and Phylogenetic Inference from Shape Data
Hyperiax supplies JAX-based tree algorithms that make BFFG feasible for parameter inference and ancestral state reconstruction from landmark shapes on phylogenies with hundreds of tips.
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Neural Backward Filtering Forward Guiding
NBFFG combines a closed-form backward filter from a linear-Gaussian proxy process with a learned neural residual to enable efficient variational inference and unbiased pathwise subsampling for nonlinear diffusions on trees.