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$\mathfrak{Michael Betancourt}$

\mathfrak{Michael Betancourt}

Mastodon @betanalpha@fediscience.org

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🔄 Data last refreshed 33 minutes ago

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Make a deal with \mathfrak{Michael Betancourt}

🔒 Paid proposals held safely in escrow — released only when the work's approved.

Followers

424

Account age

3 yrs

🧰 Free analysis for \mathfrak{Michael Betancourt}

🕵️ Fake follower check 📊 Engagement rate 💰 What they charge

📊 Post engagement

Avg engagement / post

0.5%

Engagement vs followers

Nov 2022

On Mastodon since

🔥 Top post: Remember xkcd 1132, https://xkcd.com/1132/? Prompted by some re · 2 likes + reposts

📊 Activity & format

Posting cadence

1.3 / week

A lower-frequency account — each post lands with more weight.

Content mix

Mostly text

Recent: 12 text · 0 image · 0 video.

Follower / following

12×

Follows 36 back. A strong ratio — an audience that follows them, not a follow-for-follow network.

Recent posts

View on Mastodon ↗

🔥 Top post Remember xkcd 1132, https://xkcd.com/1132/? Prompted by some recent discussion over at https://www.patreon.com/betanalpha/posts/explaining-joke-162341925, I ended up implementing frequentist and Bayesian analysis of the setup and writing up the subtle issue what I think the exa… ★ 2

Friendly reminder that everyone is welcome to join the probabilistic modeling Discord server to discuss all things probabilistic modeling, https://www.patreon.com/betanalpha/posts/generative-88674175. As always, a huge thanks to my supporters over on patreon dot com, https://www.patreon.com/c/betanalpha, who make freely available work like this feasible. Incidentally, covector+ supporters also have access to a 2.5 hour video review of … Unlike most of my public case studies, this piece builds up the analysis in Python instead of R. That said, the modeling approach is the same: build up to an adequate model iteratively, using retrodictive tension to motivate productive u… Nothing lasts forever; especially customers. In my latest case study I use survival modeling techniques, including proportional stimuli/hazards by the end, to model customer churn. HTML: https://betanalpha.github.io/assets/chapters_html/c… Conditional probability theory defines a formal way to decompose probability distributions across partitions, with a particular focus on partitions implicitly defined by the level sets of a function. When the cells of the partition are non… ★ 1 Chapter 1 - HTML: https://betanalpha.github.io/assets/chapters_html/conditional_probability_theory.html#conclusion PDF: https://betanalpha.github.io/assets/chapters_pdf/conditional_probability_theory.pdf Chapter 2 - HTML: https://betana… ★ 2 I've been updating my recent probability theory chapters, all of which can be found at https://betanalpha.github.io/writing#part1. The biggest changes have been to my conditional probability theory material, which has been expanded into tw… ★ 1 On practical asymptotics, https://www.patreon.com/betanalpha/posts/on-practical-161108640. Just dropped a new case study studying customer churn (in Python!) for my covector+ supporters over on patreon dot com, https://www.patreon.com/c/betanalpha ... Wednesday, June 3rd at 1:30 PM EDT! Statistics office hours, https://www.patreon.com/posts/spring-has-hours-157652562! I will take all statistics questions; most answers will come down to (a) model something or (b) apply Bayes' Theorem a… Alt: Do or do not, there is no do operator.

🐘 Community & instance

Home server

fediscience.org

Their home server on the fediverse — the instance a creator picks signals the community they belong to.

On Mastodon since

Nov 2022

Joined in the Twitter-exodus wave of late 2022 — part of the migration that made Mastodon a real destination.

💡 Facts

🗓️Joined Mastodon in 2022 — 3 years ago.

👁️Averages 2 views per post.

📤Posts about 1.3× per week.

🕵️ Fake follower check

Estimated

66/100

Good Credibility score

89%

Real Real audience

Low Fake-follower risk

High Data confidence

Est. 89% real, active audience · Low fake-follower risk.
Engagement (~0.5% of followers engage each post) is around typical for Mastodon.
Established account (3+ years old).

Heuristic estimate from engagement, follower ratios, account age & growth — a screening signal, not a guarantee.

About

Once and future physicist masquerading as a statistician. Reluctant geometer. Stan developer. I'm not mad that you ignore divergent transitions, I'm just disappointed. He/him.

🔀 Audience overlap

Estimated

Estimated shared audience with similar creators — useful for avoiding overlap (or doubling down) when planning a campaign.

Mani and the Nonos 18%

Klara in the hidden land 15%

Hendryk 13%

Stefan Diefenbach-Trommer (er) 12%

Hanno Zulla 10%

charlotte 10%

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\mathfrak{Michael Betancourt}

Make a deal with \mathfrak{Michael Betancourt}

📊 Post engagement

📊 Activity & format

Recent posts

🐘 Community & instance

💡 Facts

🕵️ Fake follower check

About

🔀 Audience overlap

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