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Jan-Ole Koslik @olemole.bsky.social · Sep 5

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/

Fast Numerical Maximum Likelihood Estimation for Latent Markov Models

A variety of latent Markov models, including hidden Markov models, hidden semi-Markov models, state-space models and continuous-time variants can be formulated and estimated within the same framework ...

janoleko.github.io

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Jan-Ole Koslik @olemole.bsky.social · Sep 5

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...

sagepub.com

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Reposted by Jan-Ole Koslik

Mark Cuban @mcuban.bsky.social · Apr 19

Congrats @bluesky on 35m !

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Jan-Ole Koslik @olemole.bsky.social · Mar 19

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

janoleko.github.io/LaMa/

Fast Numerical Maximum Likelihood Estimation for Latent Markov Models

A variety of latent Markov models, including hidden Markov models, hidden semi-Markov models, state-space models and continuous-time variants can be formulated and estimated within the same framework ...

janoleko.github.io

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Jan-Ole Koslik @olemole.bsky.social · Mar 19

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

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 …

www.sciencedirect.com

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Reposted by Jan-Ole Koslik

Vincent Arel-Bundock @vincentab.bsky.social · Feb 14

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...

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Reposted by Jan-Ole Koslik

Bluesky @bsky.app · Feb 6

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.

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."

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Reposted by Jan-Ole Koslik

Vianey Leos Barajas @vianeylb.bsky.social · Jan 31

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. 🧪

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Reposted by Jan-Ole Koslik

Katharina Ammann @kammann.bsky.social · Jan 21

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

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Reposted by Jan-Ole Koslik

Robert (Bob) Kubinec @rmkubinec.bsky.social · Jan 16

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

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Reposted by Jan-Ole Koslik

Sam Burgess @oceanterra.org · Jan 10

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

🌍🌡️🧪⚒️🌊

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Reposted by Jan-Ole Koslik

Sam Power @spmontecarlo.bsky.social · Jan 6

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

On Theoretical Foundations of Diffusion Models

YouTube video by C3 Digital Transformation Institute

youtu.be

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Jan-Ole Koslik @olemole.bsky.social · Jan 7

Full kaggle notebook:

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

HMMotion: Using tracking data to predict coverage

Explore and run machine learning code with Kaggle Notebooks | Using data from multiple data sources

www.kaggle.com

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Jan-Ole Koslik @olemole.bsky.social · Jan 7

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

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Reposted by Jan-Ole Koslik

Michael Lopez @statsbylopez.bsky.social · Jan 6

Big Data Bowl submissions are due tomorrow

🚨🚨

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

🚨🚨

#BigDataBowl

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Reposted by Jan-Ole Koslik

Sharon Machlis @smachlis.bsky.social · Jan 3

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...

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)

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Jan-Ole Koslik @olemole.bsky.social · Dec 30

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

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Jan-Ole Koslik @olemole.bsky.social · Dec 30

🥲🥲🥲

Reposted by Jan-Ole Koslik

Bluesky @bsky.app · Dec 30

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! 🎉