Jan-Ole Koslik

The LaMa package provides a versatile framework for inference with latent Markov models, designed to make building such models fun and efficient.

Check out the vignettes and start building models! 🛠️
👉 janoleko.github.io/LaMa/

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...

Congrats @bluesky on 35m !

All the relevant methodology is fully implemented 🛠️ in my R package LaMa 🦙:

janoleko.github.io/LaMa/

Hidden semi-Markov models with inhomogeneous state dwell-time distributions The well-established methodology for the estimation of hidden semi-Markov models (HSMMs) as hidden Markov models (HMMs) with extended state spaces is …

My paper is out! 🎉 I explore hidden semi-Markov models with covariate-dependent state dwell-time distributions — because sometimes Markov just isn’t enough.
Case study: Arctic muskox movement! 🦬📊
🔗 www.sciencedirect.com/science/arti...

#stats #TimeSeries #HSMM #StatisticalEcology #rstats

Rdatasets is a collection of 2300 free and documented datasets in CSV format. It's a great resource for teaching and exploration!

The new `get_dataset()` function from the {marginaleffects} 📦 allows you to search and load them directly in #Rstats.

vincentarelbundock.github.io/Rdatasets/ar...

Popular meme format, grandma labeled with "bluesky's public launch was one year ago today." Younger person helping her labeled with "sure grandma let's get you to bed."

happy first birthday to Bluesky, and what a year it's been!

with every day, the need for an open network that puts people first becomes increasingly clear. we're glad to be building this with you. after all, the heart of a social network is the people.

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. 🧪

Could watch this animation all day 😍

Did you know that you can create GIFs with gganimate()? They can even be embedded in a latex PDF file and played via Adobe Acorbat Reader 💥

#ggplot #gganimate #datavisualisation #statisticalmodelling #finance #economics #quants

I'm just saying this is syntatically correct #rstats code

2024 @copernicusecmwf.bsky.social #climate data out today:

📈 2024 - first year more than 1.5°C above pre-industrial; for ERA5 it was 1.6ºC
🌡️ the past 10 years were the 10 warmest years on record
📈 2024 was warmest year for all continental regions, except Antarctica and Australasia

🌍🌡️🧪⚒️🌊

YouTube video by C3 Digital Transformation Institute On Theoretical Foundations of Diffusion Models

Quite a nice watch:
youtu.be/TE4R8bumI-Q?...

Full kaggle notebook:

www.kaggle.com/code/rouvenm...

In this year‘s NFL Big Data Bowl 🏈 submission, @rmichels.bsky.social, Robert Bajons, and I employ hidden Markov models to uncover 🔎guarding assignments and use this additional information to improve the prediction of defensive strategies. 📊
#bigdatabowl #rstats

Big Data Bowl submissions are due tomorrow

🚨🚨

The deadline is 11:59 PM UTC, which is 6:59 PM EST

🚨🚨

#BigDataBowl

Example With a matrix > x <- matrix(rnorm(20 * 500), nrow = 20, ncol = 500) it is many times faster to calculate medians column by column using > mu <- matrixStats::colMedians(x) than using > mu <- apply(x, MARGIN = 2, FUN = median) Moreover, if performing calculations on a subset of rows and/or columns, using > mu <- colMedians(x, rows = 33:158, cols = 1001:3000) is much faster and more memory efficient than > mu <- apply(x[33:158, 1001:3000], MARGIN = 2, FUN = median)

The {matrixStats} #RStats 📦 “provides highly optimized functions for computing common summaries over rows and columns of matrices, e.g. rowQuantiles(). There are also functions that operate on vectors.”
By @henrikbengtsson.bsky.social
github.com/HenrikBengts...

I crunch a lot of numbers to ultimately tell people „yeah this thing may or may not happen idk“.

🥲🥲🥲

2024 In Review - Bluesky It’s been a big year for Bluesky! Let's take a look back at everything that’s happened in the past year.

What a year! Bluesky opened its doors just last February. Since then, we opened federation, launched video, rolled out trending topics, and a whole lot more.

Happy New Year, and here’s to a great year ahead! 🎉

Oh yeah, hurts every time 😂

Every time it’s called a classifier, somewhere, a statistician dies. 😵‍💫
#stats #ML

Linear Model and Extensions I developed the lecture notes based on my ``Linear Model'' course at the University of California Berkeley over the past seven years. This book provides an intermediate-level introduction to the linea...

Ooooooo

arxiv.org/abs/2401.00649

Boromir from Lord of the Rings saying "ONE DOES NOT SIMPLY INSTALL PYTHON"

Does probability exist? Probably not — but it is useful to act as if it does.

www.nature.com/articles/d41...

May I be added? Working a lot on hidden Markov models for movement ecology. 😄

🛠️ The paper is complemented by the R package LaMa, a fast ⚡ and flexible framework for estimating latent Markov models, designed to make building such models much easier for everyone!

👉 janoleko.github.io/LaMa/

Check out the vignettes and start building models! 🏗️

How to build your latent Markov model -- the role of time and space 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...

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

A log likelihood is shown. The quantities for the score, wald, and likelihood tests are illustrated with respect to the log likelihood.

You will be visited by three ghosts

Quarto An open source technical publishing system for creating beautiful articles, websites, blogs, books, slides, and more. Supports Python, R, Julia, and JavaScript.

I feel like not enough people know about Quarto for creating documents.

How it works: Write in markdown and use Quarto to convert it to html, pdf, epub, ...

I produce my books with Quarto (web + ebook + print version). But you can also use it for websites, reports, dashboards, ...

quarto.org