SMC forgets its initial condition geometrically in the jump chain and as 1/ℓ in continuous genetic distance, justifying independent-locus approximations.
arXiv preprint arXiv:2509.25441 , year=
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Mixtures of convolutional measures on low-dimensional affine spaces admit unique identifiability in semi-parametric settings and posterior contraction rates under convex polytope support assumptions in a well-specified Bayesian regime.
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Rates of forgetting for the sequentially Markov coalescent
SMC forgets its initial condition geometrically in the jump chain and as 1/ℓ in continuous genetic distance, justifying independent-locus approximations.
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Learning Mixtures of Nonparametric and Convolutional Measures on Effectively Low-dimensional Affine Spaces
Mixtures of convolutional measures on low-dimensional affine spaces admit unique identifiability in semi-parametric settings and posterior contraction rates under convex polytope support assumptions in a well-specified Bayesian regime.