New preprint 📑 Fast inference in HMMs with latent Gaussian fields (via SPDE approach + RTMB) ⚡️ 🔗 arxiv.org/abs/2603.17469 We modify the forward algorithm to recover a sparse Hessian ➡️ Fast automatic Laplace approximation Case studies: 1) Detecting stellar flares 2) Lion movement w spatial field

We have a new preprint on covariate-driven #HMMs! doi.org/10.48550/arX... @olemole.bsky.social, @rolandlangrock.bsky.social • commonly used hypothetical stationary distribution can be biased⚠️ • we propose 2 approaches allowing unbiased inference • simulations and case study on Galápagos tortoises🐢🗺️

Very proud of this paper, where we show that what I've been teaching folks for years is actually really not such a clever thing to do 🙈 But we also provide solutions 💪 Also what a way to kick-start your PhD, @mayavienken.bsky.social 👑

Our paper on #HMMs with periodically ⏰ varying transition probabilities is published! 🎉 @carlinafeldmann.bsky.social, Sina Mews, @rmichels.bsky.social @rolandlangrock.bsky.social doi.org/10.1214/25-AOAS2107 We derive the periodically #stationary distribution and the implied dwell-time distribution

Our review paper on latent Markov models is now published in Statistical Modelling! 🎉 @rolandlangrock.bsky.social @SinaMews. We discuss choosing the right time and space formulation and provide the R package 📦 LaMa for fast ⚡and flexible estimation. 📄 Paper: journals.sagepub.com/eprint/UETXX...

The world is on 🔥 -- and here's my first publication in an astronomy journal: iopscience.iop.org/article/10.3... We combine Gaussian processes + hidden Markov models to efficiently detect stellar flares in one modelling step. 🧪

Sina Mews, Roland Langrock, and I have updated 🆕 our review paper! It offers a comprehensive overview on choosing the right time ⏰ and space 📏 formulation for latent Markov models, providing a unifying perspective on discrete- and continuous-time HMMs, SSMs and MMPPs. 👉 arxiv.org/abs/2406.19157