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Followers
350
Account age
3 yrs
💰 Suggested sponsorship rate
$40 – $65 / sponsored post
Estimated from audience size & typical reach — every deal is negotiable and held in SocialDB escrow.
🎁 Free analysisfor Pierre-Simon Laplace
Known for
4 views
🎙️ New episode alert!
In this episode Alex Andorra & Stefan Radev dive into amortized inference, train a neural net once on sims, deploy on real data as many times as you want. They cover sim-to-real, psych & neuro as test beds, honest failure modes and more ...
https://lnkd.in/dCY85k4g
4 views
🎙️ New episode of Learning Bayesian Statistics!
EP 155 with Alex Andorra & Andreas Munk, why Bayesian inference still hasn't broken into everyday use. The barrier isn't the math, it's the mental shift
🔗 https://lnkd.in/gchb6bqj
#Bayesian #ProbablisticProgramming
3 views
Most stats thinking starts with a dataset.
Bayesian experimental design asks: which data should you collect first?
Ep 156 of Learning Bayesian Statistics with Alex Andorra and Adam Foster covers:
👉Expected information gain
👉BALD
👉Deep adaptive design and more ...
🎧 https://lnkd.in/ebjV9xXS
#BayesianStatistics #ExperimentalDesign #BayesianInference #ActiveLearning
1 views
Bayesian methods aren't going away in the age of LLMs.
Christopher Krapu joins Alex Andorra to discuss GPUs, Gaussian Processes, probabilistic AI and more!
🎧https://learnbayesstats.com/episode/bayesian-hydrology-differentiable-inference-gpu-pymc-christopher-krapu
#bayesian #GPU #AI #LLM #Gaussianprocess #probablisticai
📊 Post engagement
Recent posts · likes + reposts
Feb 2latest
2
Avg engagement / post
1–3
Typical post (engagement)
0.6%
Engagement vs followers
69/100
Consistent reach
Nov 2022
On Mastodon since
Half of recent posts land between 1 and 3 likes + reposts (median 1) — a dependable floor a sponsored post can count on.
🔥 Top post: 🎙️ New episode alert! In this episode Alex Andorra & Stefan Rad · 4 likes + reposts
📊 Activity & format
Posting cadence
0.43 / week
A lower-frequency account — each post lands with more weight.
Content mix
Mostly images
Recent: 0 text · 11 image · 1 video.
Recent posts
View on Mastodon ↗
🔥 Top post
🎙️ New episode alert!
In this episode Alex Andorra & Stefan Radev dive into amortized inference, train a neural net once on sims, deploy on real data as many times as you want. They cover sim-to-real, psych & neuro as test beds, honest failure modes and more ...
https://lnkd.in…
Bayesian methods aren't going away in the age of LLMs.
Christopher Krapu joins Alex Andorra to discuss GPUs, Gaussian Processes, probabilistic AI and more!
🎧https://learnbayesstats.com/episode/bayesian-hydrology-differentiable-inference-gp…
Episode 161 is out 🎧 In which Luigi Acerbi explains why transformers are secretly neural processes, how his Amortized Conditioning Engine unifies inference and prediction, and why "amortize everything" needed a rethink.
🔗https://learnbaye…
🎙️ Bayesian epistemology is Bayesian statistics minus the statistics.
In ep 160 Vaden Masrani joins Alex Andorra to talk about why Bayes' theorem is great with real data, why it breaks down on one-off future events with nothing to count👇
…
🎙️ New episode!
Alex Andorra sits down with Matthijs Hollanders on Bayesian occupancy models for wildlife data - what they are, why camera traps break classical approaches, and how his occARU R package handles it with hierarchical GPs and…
Episode 158 is out 🎙️
In this episode Alex Andorra sits down with Stefan Radev to talk amortized Bayesian inference, multiverse analysis, and what a foundation model for Bayesian inference should actually look like and more..
🔗 https://lnk…
Most stats thinking starts with a dataset.
Bayesian experimental design asks: which data should you collect first?
Ep 156 of Learning Bayesian Statistics with Alex Andorra and Adam Foster covers:
👉Expected information gain
👉BALD
👉Deep ada…
🎙️ New episode of Learning Bayesian Statistics!
EP 155 with Alex Andorra & Andreas Munk, why Bayesian inference still hasn't broken into everyday use. The barrier isn't the math, it's the mental shift
🔗 https://lnkd.in/gchb6bqj
#Bayesian …
Episode 152 is out 🎙️
Host Alex Andorra talks with Daniel Saunders about a Bayesian decision theory workflow.
Big idea: stop optimizing for model accuracy and start optimizing for decision value.
🔗 https://lnkd.in/gw_uGaZc
#Bayesian #Dec…
🎙️ New episode out now - Episode 151: Diffusion Models in Python, a Live Demo
In this episode, Alex Andorra is joined by Jonas Arruda to explore how diffusion models can be used for simulation-based inference (SBI) in practice with a live …
▶
Bayesian neural networks need one thing to matter: good uncertainty. Scaling them has always been the hard part
In this episode, host Alex Andorra with Emmanuel Sommer, Jakob Robnik, and David Rügamer explain what’s changing, faster sampli…
Work in tech is changing fast, and not always in obvious ways.
Alex Andorra talks with Alana Karen about how AI, hiring, and management are reshaping careers behind the scenes, AI automating early work, hiring favoring familiarity … and mo…
🐘 Community & instance
Home server
mstdn.science
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 0.4× per week.
🕵️ Fake follower check
Estimated44/100
Fair
Credibility score
73%
Mixed
Real audience
Medium
Fake-follower risk
Medium
Data confidence
- Est. 73% real, active audience · Medium fake-follower risk.
- Low engagement (~0.6% of followers engage each post) — a sign of an inflated or inactive audience.
- Established account (3+ years old).
Heuristic estimate from engagement, follower ratios, account age & growth — a screening signal, not a guarantee.
About
A #podcast on #Bayesian #Statistics -- the methods, the projects, the people
By Alexandre Andorra
#StatsTodon #BayesianStats #Science
📸 Gallery
🔀 Audience overlap
EstimatedEstimated shared audience with similar creators — useful for avoiding overlap (or doubling down) when planning a campaign.
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