Statistician @BielefeldUniversity, working mostly on HMMs, statistical ecology, sports data. But teaching is even more fun.
Roland Langrock
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 👑
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. 🧪
We are looking for participants for our study World Cup Fever, which aims to investigate the physiological responses of fans from different nationalities to the course of matches. Please share widely 🙏
www.uni-bielefeld.de/einrichtunge...
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🐢🗺️
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
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...
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
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🐢🗺️
Maya Vienken
sagepub.com
Statistical models that involve latent Markovian state processes have become immensely popular tools for analysing time series and other sequential data. However, the plethora of model formulations, t...
