By Dr Lachlan Astfalck

Preprints

Sharrock L; Astfalck L; Moss H, 2026, LatentFlow: A General Framework for Conditioning Stochastic Processes, http://dx.doi.org/10.48550/arxiv.2607.12922

Zhang R-Y; Astfalck L; Cripps E; Leslie D; Moss H, 2026, Dynamic Gaussian Processes and the Vanilla-SPDE Exchange, http://dx.doi.org/10.48550/arxiv.2606.31063

Zhang R-Y; Astfalck L; Cripps E; Leslie DS; Moss HB, 2026, BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields, http://dx.doi.org/10.48550/arxiv.2509.26005

Moss H; Astfalck L; Cowperthwaite T; Doumont C; Willis S; Hennig P; Nemeth C; Zammit-Mangion A, 2026, Conditioning Gaussian Processes on Almost Anything, http://dx.doi.org/10.48550/arxiv.2605.21041

Astfalck L; Sen D; Patra S; Cripps E; Dunson D, 2026, Posterior Projection for Inference in Constrained Spaces, http://dx.doi.org/10.48550/arxiv.1812.05741

Milne I; Astfalck L; Zed M; Lee-Kopij J; Cripps E, 2026, Hybrid physics-data driven spectral forecasts of semisubmersible response, http://dx.doi.org/10.48550/arxiv.2603.26026

Ou R; Astfalck L; Sen D; Dunson D, 2025, Scalable Bayesian inference for time series via divide-and-conquer, http://dx.doi.org/10.48550/arxiv.2106.11043

Tang T; Astfalck L; Dunson D, 2025, Efficient Bayesian Inference for Discretely Observed Continuous Time Markov Chains, http://dx.doi.org/10.48550/arxiv.2507.16756

Astfalck L, 2025, Universal Modelling of Autocovariance Functions via Spline Kernels, http://dx.doi.org/10.48550/arxiv.2506.21953

Astfalck L; Sykulski A; Cripps E, 2024, Bias correction of quadratic spectral estimators, http://dx.doi.org/10.48550/arxiv.2410.12386

Astfalck L; Bird C; Williamson D, 2024, Generalised Bayes Linear Inference, http://dx.doi.org/10.48550/arxiv.2405.14145

Astfalck LC; Sykulski AM; Cripps EJ, 2024, Debiasing Welch's Method for Spectral Density Estimation, http://dx.doi.org/10.48550/arxiv.2312.13643

Astfalck L; Williamson D; Gandy N; Gregoire L; Ivanovic R, 2023, Coexchangeable process modelling for uncertainty quantification in joint climate reconstruction, http://dx.doi.org/10.48550/arxiv.2111.12283

Patterson VL; Gregoire LJ; Ivanovic R; Gandy N; Owen J; Smith RS; Pollard OG; Astfalck LC, Contrasting the Penultimate and Last Glacial Maxima (140 and 21 ka BP) using coupled climate-ice sheet modelling, http://dx.doi.org/10.5194/cp-2024-10

Gandy N; Astfalck LC; Gregoire LJ; F IR; Patterson VL; Sam S-T; Smith RS; Danny W; Rigby R, De-tuning a coupled Climate Ice Sheet Model to simulate the North American Ice Sheet at the Last Glacial Maximum, http://dx.doi.org/10.1002/essoar.10512201.1

Pollard O; Barlow N; Gregoire LJ; Gomez N; Astfalck LC; McGuire A, Identifying the Antarctic melt contribution to Last Interglacial sea level from Eurasian relative sea-level records, http://dx.doi.org/10.31223/x5vf6x

Ponte ALS; Astfalck L; Rayson M; Zulberti A; Jones N, Inferring flow energy, space and time scales: freely-drifting vs fixed point observations, http://dx.doi.org/10.5194/npg-2024-10

Robinson S; Ivanovic R; Gregoire L; Astfalck L; van de Flierdt T; Plancherel Y; Pöppelmeier F; Tachikawa K, Optimisation of the marine Nd isotope scheme in the ocean component of the FAMOUS general circulation model, http://dx.doi.org/10.5194/egusphere-2022-937

Pollard OG; Barlow NLM; Gregoire L; Gomez N; Cartelle V; Ely JC; Astfalck LC, Quantifying the Uncertainty in the Eurasian Ice-Sheet Geometry at the Penultimate Glacial Maximum (Marine Isotope Stage 6), http://dx.doi.org/10.5194/tc-2023-5

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