#markovchains — Public Fediverse posts

Beginner #MarkovChains question:
I'm working on a small analysis program for a friend. It's already existing code where you select a number of labels from a labelled sequence of interactions and then supposedly get a transition matrix from them.

BUT - the code ignores intermediate labels when counting transitions.
e.g. if a sequence has three labelled members w,x,y, and you only want to analyse w, and y, the current code would count that as a transition from w to y, ignoring the intervening x.

This does not seem appropriate for calculating transition probabilities. If I understand correctly, this should not be counted at all, since the transition was w -x, then x-y.

Does that sound like I have understood correctly?

Beginner #MarkovChains question:
I'm working on a small analysis program for a friend. It's already existing code where you select a number of labels from a labelled sequence of interactions and then supposedly get a transition matrix from them.

BUT - the code ignores intermediate labels when counting transitions.
e.g. if a sequence has three labelled members w,x,y, and you only want to analyse w, and y, the current code would count that as a transition from w to y, ignoring the intervening x.

This does not seem appropriate for calculating transition probabilities. If I understand correctly, this should not be counted at all, since the transition was w -x, then x-y.

Does that sound like I have understood correctly?

Beginner #MarkovChains question:
I'm working on a small analysis program for a friend. It's already existing code where you select a number of labels from a labelled sequence of interactions and then supposedly get a transition matrix from them.

BUT - the code ignores intermediate labels when counting transitions.
e.g. if a sequence has three labelled members w,x,y, and you only want to analyse w, and y, the current code would count that as a transition from w to y, ignoring the intervening x.

This does not seem appropriate for calculating transition probabilities. If I understand correctly, this should not be counted at all, since the transition was w -x, then x-y.

Does that sound like I have understood correctly?

Beginner #MarkovChains question:
I'm working on a small analysis program for a friend. It's already existing code where you select a number of labels from a labelled sequence of interactions and then supposedly get a transition matrix from them.

BUT - the code ignores intermediate labels when counting transitions.
e.g. if a sequence has three labelled members w,x,y, and you only want to analyse w, and y, the current code would count that as a transition from w to y, ignoring the intervening x.

This does not seem appropriate for calculating transition probabilities. If I understand correctly, this should not be counted at all, since the transition was w -x, then x-y.

Does that sound like I have understood correctly?

🚨 BREAKING: High school essay from 2012 declares *Markov Chains* as the OG language models! 🙄 Apparently, *Linear Algebra 101* had all the answers before #AI got cool. Who knew teenage musings could redefine the hype cycle? 😂
https://elijahpotter.dev/articles/markov_chains_are_the_original_language_models #MarkovChains #LinearAlgebra #HypeCycle #TeenageMusings #HackerNews #ngated

🚨 BREAKING: High school essay from 2012 declares *Markov Chains* as the OG language models! 🙄 Apparently, *Linear Algebra 101* had all the answers before #AI got cool. Who knew teenage musings could redefine the hype cycle? 😂
https://elijahpotter.dev/articles/markov_chains_are_the_original_language_models #MarkovChains #LinearAlgebra #HypeCycle #TeenageMusings #HackerNews #ngated

🚨 BREAKING: High school essay from 2012 declares *Markov Chains* as the OG language models! 🙄 Apparently, *Linear Algebra 101* had all the answers before #AI got cool. Who knew teenage musings could redefine the hype cycle? 😂
https://elijahpotter.dev/articles/markov_chains_are_the_original_language_models #MarkovChains #LinearAlgebra #HypeCycle #TeenageMusings #HackerNews #ngated

🚨 BREAKING: High school essay from 2012 declares *Markov Chains* as the OG language models! 🙄 Apparently, *Linear Algebra 101* had all the answers before #AI got cool. Who knew teenage musings could redefine the hype cycle? 😂
https://elijahpotter.dev/articles/markov_chains_are_the_original_language_models #MarkovChains #LinearAlgebra #HypeCycle #TeenageMusings #HackerNews #ngated

Markov Chains Are the Original Language Models

https://elijahpotter.dev/articles/markov_chains_are_the_original_language_models

#HackerNews #MarkovChains #LanguageModels #AIResearch #NaturalLanguageProcessing #MachineLearning

Markov Chains Are the Original Language Models

https://elijahpotter.dev/articles/markov_chains_are_the_original_language_models

#HackerNews #MarkovChains #LanguageModels #AIResearch #NaturalLanguageProcessing #MachineLearning

Markov Chains Are the Original Language Models

https://elijahpotter.dev/articles/markov_chains_are_the_original_language_models

#HackerNews #MarkovChains #LanguageModels #AIResearch #NaturalLanguageProcessing #MachineLearning

Markov Chains Are the Original Language Models

https://elijahpotter.dev/articles/markov_chains_are_the_original_language_models

#HackerNews #MarkovChains #LanguageModels #AIResearch #NaturalLanguageProcessing #MachineLearning

Markov Chains Are the Original Language Models

https://elijahpotter.dev/articles/markov_chains_are_the_original_language_models

#HackerNews #MarkovChains #LanguageModels #AIResearch #NaturalLanguageProcessing #MachineLearning

What I’ve been reading (and watching) this week ending 24 August 2025 https://jchyip.medium.com/what-ive-been-reading-and-watching-this-week-ending-24-august-2025-8681649d3972 #leadership #SystemsThinking #MarkovChains #innovation

What I’ve been reading (and watching) this week ending 24 August 2025 https://jchyip.medium.com/what-ive-been-reading-and-watching-this-week-ending-24-august-2025-8681649d3972 #leadership #SystemsThinking #MarkovChains #innovation

What I’ve been reading (and watching) this week ending 24 August 2025 https://jchyip.medium.com/what-ive-been-reading-and-watching-this-week-ending-24-august-2025-8681649d3972 #leadership #SystemsThinking #MarkovChains #innovation

What I’ve been reading (and watching) this week ending 24 August 2025 https://jchyip.medium.com/what-ive-been-reading-and-watching-this-week-ending-24-august-2025-8681649d3972 #leadership #SystemsThinking #MarkovChains #innovation

What I’ve been reading (and watching) this week ending 24 August 2025 https://jchyip.medium.com/what-ive-been-reading-and-watching-this-week-ending-24-august-2025-8681649d3972 #leadership #SystemsThinking #MarkovChains #innovation

https://youtube.com/watch?v=KZeIEiBrT_w&si=2sFxXqPnh2cLIOKF

Interessante Zusammenfassung über #MarkovChains

https://youtube.com/watch?v=KZeIEiBrT_w&si=2sFxXqPnh2cLIOKF

Interessante Zusammenfassung über #MarkovChains

The Strange #Math That Predicts (Almost) Anything https://youtube.com/watch?v=KZeIEiBrT_w&si=Jn6_OtAzfB4zTE0i #MarkovChains

The Strange #Math That Predicts (Almost) Anything https://youtube.com/watch?v=KZeIEiBrT_w&si=Jn6_OtAzfB4zTE0i #MarkovChains

The Strange #Math That Predicts (Almost) Anything https://youtube.com/watch?v=KZeIEiBrT_w&si=Jn6_OtAzfB4zTE0i #MarkovChains

The Strange #Math That Predicts (Almost) Anything https://youtube.com/watch?v=KZeIEiBrT_w&si=Jn6_OtAzfB4zTE0i #MarkovChains

The Strange #Math That Predicts (Almost) Anything https://youtube.com/watch?v=KZeIEiBrT_w&si=Jn6_OtAzfB4zTE0i #MarkovChains

🎉 Mégra 0.0.16 is out! Just some small improvements I found while preparing for a workshop.

Thanks @alice_ahora for improving the tutorial with me!

https://github.com/the-drunk-coder/megra.rs/releases/tag/v0.0.16

https://megra-doc.readthedocs.io/en/latest/

:megra_wavetable:

#livecoding #markovchains #rust

🎉 Mégra 0.0.16 is out! Just some small improvements I found while preparing for a workshop.

Thanks @alice_ahora for improving the tutorial with me!

https://github.com/the-drunk-coder/megra.rs/releases/tag/v0.0.16

https://megra-doc.readthedocs.io/en/latest/

:megra_wavetable:

#livecoding #markovchains #rust

🎉 Mégra 0.0.16 is out! Just some small improvements I found while preparing for a workshop.

Thanks @alice_ahora for improving the tutorial with me!

https://github.com/the-drunk-coder/megra.rs/releases/tag/v0.0.16

https://megra-doc.readthedocs.io/en/latest/

:megra_wavetable:

#livecoding #markovchains #rust

🎉 Mégra 0.0.16 is out! Just some small improvements I found while preparing for a workshop.

Thanks @alice_ahora for improving the tutorial with me!

https://github.com/the-drunk-coder/megra.rs/releases/tag/v0.0.16

https://megra-doc.readthedocs.io/en/latest/

:megra_wavetable:

#livecoding #markovchains #rust

🎉 Mégra 0.0.16 is out! Just some small improvements I found while preparing for a workshop.

Thanks @alice_ahora for improving the tutorial with me!

https://github.com/the-drunk-coder/megra.rs/releases/tag/v0.0.16

https://megra-doc.readthedocs.io/en/latest/

:megra_wavetable:

#livecoding #markovchains #rust

Rock, Paper, Scissors that learns how you play using markov chains

https://luduxia.com/showdown/

#HackerNews #RockPaperScissors #MarkovChains #MachineLearning #GameDevelopment #AI

Rock, Paper, Scissors that learns how you play using markov chains

https://luduxia.com/showdown/

#HackerNews #RockPaperScissors #MarkovChains #MachineLearning #GameDevelopment #AI

Rock, Paper, Scissors that learns how you play using markov chains

https://luduxia.com/showdown/

#HackerNews #RockPaperScissors #MarkovChains #MachineLearning #GameDevelopment #AI

Rock, Paper, Scissors that learns how you play using markov chains

https://luduxia.com/showdown/

#HackerNews #RockPaperScissors #MarkovChains #MachineLearning #GameDevelopment #AI

Markov Chains Explained Visually (2014) — https://setosa.io/ev/markov-chains/
#HackerNews #MarkovChains #VisualLearning #DataScience #Educational #Content

Markov Chains Explained Visually (2014) — https://setosa.io/ev/markov-chains/
#HackerNews #MarkovChains #VisualLearning #DataScience #Educational #Content

Markov Chains Explained Visually (2014) — https://setosa.io/ev/markov-chains/
#HackerNews #MarkovChains #VisualLearning #DataScience #Educational #Content

Markov Chains Explained Visually (2014) — https://setosa.io/ev/markov-chains/
#HackerNews #MarkovChains #VisualLearning #DataScience #Educational #Content

Markov Chains Explained Visually (2014) — https://setosa.io/ev/markov-chains/
#HackerNews #MarkovChains #VisualLearning #DataScience #Educational #Content

Teaching ChatGPT-4o is a great way to learn.

It's always nice to notice you know something ChatGPT doesn't know, as it typically means you know something most specialists in the field don't know:
https://chatgpt.com/share/67ac8053-bcf8-8002-951a-89bda381d3ff

#LLM #mathematics #MarkovChains #MDPs

Teaching ChatGPT-4o is a great way to learn.

It's always nice to notice you know something ChatGPT doesn't know, as it typically means you know something most specialists in the field don't know:
https://chatgpt.com/share/67ac8053-bcf8-8002-951a-89bda381d3ff

#LLM #mathematics #MarkovChains #MDPs

Teaching ChatGPT-4o is a great way to learn.

It's always nice to notice you know something ChatGPT doesn't know, as it typically means you know something most specialists in the field don't know:
https://chatgpt.com/share/67ac8053-bcf8-8002-951a-89bda381d3ff

#LLM #mathematics #MarkovChains #MDPs

Teaching ChatGPT-4o is a great way to learn.

It's always nice to notice you know something ChatGPT doesn't know, as it typically means you know something most specialists in the field don't know:
https://chatgpt.com/share/67ac8053-bcf8-8002-951a-89bda381d3ff

#LLM #mathematics #MarkovChains #MDPs

Teaching ChatGPT-4o is a great way to learn.

It's always nice to notice you know something ChatGPT doesn't know, as it typically means you know something most specialists in the field don't know:
https://chatgpt.com/share/67ac8053-bcf8-8002-951a-89bda381d3ff

#LLM #mathematics #MarkovChains #MDPs

Two New Publications at the Open Journal of Astrophysics

It’s Saturday morning again so here’s another report on activity at the  Open Journal of Astrophysics.  Since the last update we have published two more papers, taking  the count in Volume 7 (2024) up to 95 and the total published by OJAp up to 210.  We’ve still got a few in the pipeline waiting for the final versions to appear on arXiv so I expect we’ll reach the 100 mark for 2024 in the next couple of weeks.

The first paper of the most recent pair, published on October 22 2024,  and in the folder marked Astrophysics of Galaxies, is “Cloud Collision Signatures in the Central Molecular Zone”  by Rees A. Barnes and Felix D. Priestley (Cardiff University, UK) .  This paper presents an analysis of combined hydrodynamical, chemical and radiative transfer simulations of cloud collisions in the Galactic disk and Central Molecular Zone (CMZ).

Here is a screen grab of the overlay which includes the abstract:

You can click on the image of the overlay to make it larger should you wish to do so.  You can find the officially accepted version of this paper on the arXiv here.

The second paper has the title “Partition function approach to non-Gaussian likelihoods: macrocanonical partitions and replicating Markov-chains” and was published October 25th 2024. The authors are Maximilian Philipp Herzog, Heinrich von Campe, Rebecca Maria Kuntz, Lennart Röver and Björn Malte Schäfe (all of Heidelberg University, Germany). This paper, which is in  the folder marked Cosmology and NonGalactic Astrophysics, describes a method of macrocanonical sampling for Bayesian statistical inference, based on the macrocanonical partition function, with applications to cosmology.

Here is a screen grab of the overlay which includes the abstract:

You can click on the image of the overlay to make it larger should you wish to do so. You can find the officially accepted version of the paper on the arXiv here.

That concludes this week’s update. More  next week!

#arXiv231116218v3 #arXiv240721575v2 #AstrophysicsOfGalaxies #BayesianInference #CosmologyAndNonGalacticAstrophysics #likelihoods #MarkovChains #MolecularCouds #PartitionFunction #starFormation #thermodynamics

Two New Publications at the Open Journal of Astrophysics

It’s Saturday morning again so here’s another report on activity at the  Open Journal of Astrophysics.  Since the last update we have published two more papers, taking  the count in Volume 7 (2024) up to 95 and the total published by OJAp up to 210.  We’ve still got a few in the pipeline waiting for the final versions to appear on arXiv so I expect we’ll reach the 100 mark for 2024 in the next couple of weeks.

The first paper of the most recent pair, published on October 22 2024,  and in the folder marked Astrophysics of Galaxies, is “Cloud Collision Signatures in the Central Molecular Zone”  by Rees A. Barnes and Felix D. Priestley (Cardiff University, UK) .  This paper presents an analysis of combined hydrodynamical, chemical and radiative transfer simulations of cloud collisions in the Galactic disk and Central Molecular Zone (CMZ).

Here is a screen grab of the overlay which includes the abstract:

You can click on the image of the overlay to make it larger should you wish to do so.  You can find the officially accepted version of this paper on the arXiv here.

The second paper has the title “Partition function approach to non-Gaussian likelihoods: macrocanonical partitions and replicating Markov-chains” and was published October 25th 2024. The authors are Maximilian Philipp Herzog, Heinrich von Campe, Rebecca Maria Kuntz, Lennart Röver and Björn Malte Schäfe (all of Heidelberg University, Germany). This paper, which is in  the folder marked Cosmology and NonGalactic Astrophysics, describes a method of macrocanonical sampling for Bayesian statistical inference, based on the macrocanonical partition function, with applications to cosmology.

Here is a screen grab of the overlay which includes the abstract:

You can click on the image of the overlay to make it larger should you wish to do so. You can find the officially accepted version of the paper on the arXiv here.

That concludes this week’s update. More  next week!

#arXiv231116218v3 #arXiv240721575v2 #AstrophysicsOfGalaxies #BayesianInference #CosmologyAndNonGalacticAstrophysics #likelihoods #MarkovChains #MolecularCouds #PartitionFunction #starFormation #thermodynamics

A drunk man will find his way home, but a drunk bird may get lost forever.” What is this sentence about?

In 2D, the random walk is “recurrent”, i.e. you are guaranteed to go back to where you started; but in 3D, the random walk is “transient”, the opposite of “recurrent”. In fact, for the 2D case, that also means that you are guaranteed to go to ALL places in the world (the only constraint is, of course, time). [Think about why.]

Markov chains are also an important tool in modelling the real world, and so I feel like this is a good excuse for bringing it up.

At the end, I also compare this phenomenon to Stein’s paradox – in both cases, there is a cutoff between 2 and 3 dimensions, and they have similar intuitive explanation – is that a coincidence?

Random walks in 2D and 3D are fundamentally different

#MarkovChain #MarkovChains #Math #Mathematics #nowWatching #randomWalk #randomness #StochasticProcess #YouTube

A drunk man will find his way home, but a drunk bird may get lost forever.” What is this sentence about?

In 2D, the random walk is “recurrent”, i.e. you are guaranteed to go back to where you started; but in 3D, the random walk is “transient”, the opposite of “recurrent”. In fact, for the 2D case, that also means that you are guaranteed to go to ALL places in the world (the only constraint is, of course, time). [Think about why.]

Markov chains are also an important tool in modelling the real world, and so I feel like this is a good excuse for bringing it up.

At the end, I also compare this phenomenon to Stein’s paradox – in both cases, there is a cutoff between 2 and 3 dimensions, and they have similar intuitive explanation – is that a coincidence?

Random walks in 2D and 3D are fundamentally different

#MarkovChain #MarkovChains #Math #Mathematics #nowWatching #randomWalk #randomness #StochasticProcess #YouTube

A drunk man will find his way home, but a drunk bird may get lost forever.” What is this sentence about?

In 2D, the random walk is “recurrent”, i.e. you are guaranteed to go back to where you started; but in 3D, the random walk is “transient”, the opposite of “recurrent”. In fact, for the 2D case, that also means that you are guaranteed to go to ALL places in the world (the only constraint is, of course, time). [Think about why.]

Markov chains are also an important tool in modelling the real world, and so I feel like this is a good excuse for bringing it up.

At the end, I also compare this phenomenon to Stein’s paradox – in both cases, there is a cutoff between 2 and 3 dimensions, and they have similar intuitive explanation – is that a coincidence?

Random walks in 2D and 3D are fundamentally different

#MarkovChain #MarkovChains #Math #Mathematics #nowWatching #randomWalk #randomness #StochasticProcess #YouTube

So the results are fun to browse, but sometimes a bit too nonsensical.

Anyone experienced with #MarkovChains able to share any tips on how to make the output better?

#CreativeCoding #GenerativeText