Raúl Peralta Lozada
@raulpl25.bsky.social
Data scientist interested in causal inference, Bayesian statistics and data visualization.
Reposted by Raúl Peralta Lozada
Here is the recording of my talk
PyData Berlin 2025: Introduction to Stochastic Variational Inference with NumPyro
Notebook: juanitorduz.github.io/intro_svi/
youtu.be/wG0no-mUMf0?...
#pydata #berlin #bayes
PyData Berlin 2025: Introduction to Stochastic Variational Inference with NumPyro
Notebook: juanitorduz.github.io/intro_svi/
youtu.be/wG0no-mUMf0?...
#pydata #berlin #bayes
Scaling Probabilistic Models with Variational Inference
YouTube video by PyData
youtu.be
November 23, 2025 at 6:18 PM
Here is the recording of my talk
PyData Berlin 2025: Introduction to Stochastic Variational Inference with NumPyro
Notebook: juanitorduz.github.io/intro_svi/
youtu.be/wG0no-mUMf0?...
#pydata #berlin #bayes
PyData Berlin 2025: Introduction to Stochastic Variational Inference with NumPyro
Notebook: juanitorduz.github.io/intro_svi/
youtu.be/wG0no-mUMf0?...
#pydata #berlin #bayes
Reposted by Raúl Peralta Lozada
Simulation-based inference (SBI) has transformed parameter inference across a wide range of domains. To help practitioners get started and make the most of these methods, we joined forces with researchers from many institutions and wrote a practical guide to SBI.
📄 Paper: arxiv.org/abs/2508.12939
📄 Paper: arxiv.org/abs/2508.12939
Simulation-Based Inference: A Practical Guide
A central challenge in many areas of science and engineering is to identify model parameters that are consistent with prior knowledge and empirical data. Bayesian inference offers a principled framewo...
arxiv.org
November 21, 2025 at 3:08 PM
Simulation-based inference (SBI) has transformed parameter inference across a wide range of domains. To help practitioners get started and make the most of these methods, we joined forces with researchers from many institutions and wrote a practical guide to SBI.
📄 Paper: arxiv.org/abs/2508.12939
📄 Paper: arxiv.org/abs/2508.12939
Reposted by Raúl Peralta Lozada
Bill Engels brought Gaussian Processes to life at PyData Seattle 2025.
From hierarchical models to a baseball performance case study, this #PyMC-powered talk shows how to model uncertainty with confidence.
Watch here: dub.link/Qm1q9ju
From hierarchical models to a baseball performance case study, this #PyMC-powered talk shows how to model uncertainty with confidence.
Watch here: dub.link/Qm1q9ju
Bill Engels - Actually using GPs in practice with PyMC | PyData Seattle 2025
Enjoy the videos and music you love, upload original content, and share it all with friends, family, and the world on YouTube.
dub.link
November 17, 2025 at 4:46 PM
Bill Engels brought Gaussian Processes to life at PyData Seattle 2025.
From hierarchical models to a baseball performance case study, this #PyMC-powered talk shows how to model uncertainty with confidence.
Watch here: dub.link/Qm1q9ju
From hierarchical models to a baseball performance case study, this #PyMC-powered talk shows how to model uncertainty with confidence.
Watch here: dub.link/Qm1q9ju
Reposted by Raúl Peralta Lozada
Trying to learn more about fixed effects. I wrote this for me, maybe this is useful for you too dpananos.github.io/posts/2025-1...
Demetri Pananos Ph.D - How to Fit a Generalized Linear Model with Fixed Effects (Pt 1)
dpananos.github.io
November 13, 2025 at 6:42 PM
Trying to learn more about fixed effects. I wrote this for me, maybe this is useful for you too dpananos.github.io/posts/2025-1...
Reposted by Raúl Peralta Lozada
The newest chapter of Think Linear Algebra is up now!
It is about least squares regression, QR decomposition, and orthogonality:
allendowney.github.io/ThinkLinearA...
It is about least squares regression, QR decomposition, and orthogonality:
allendowney.github.io/ThinkLinearA...
October 29, 2025 at 2:30 PM
The newest chapter of Think Linear Algebra is up now!
It is about least squares regression, QR decomposition, and orthogonality:
allendowney.github.io/ThinkLinearA...
It is about least squares regression, QR decomposition, and orthogonality:
allendowney.github.io/ThinkLinearA...
Reposted by Raúl Peralta Lozada
🎉 The program for this year's Causal Data Science Meeting (#CDSM2025) is now live!
📅 Nov 12–13, 2025 | 💻 Online | 🎟️ Free registration
Join us for two days of talks and debates at the intersection of causality, data science, and AI.
👉 causalscience.org
📅 Nov 12–13, 2025 | 💻 Online | 🎟️ Free registration
Join us for two days of talks and debates at the intersection of causality, data science, and AI.
👉 causalscience.org
October 23, 2025 at 1:48 PM
🎉 The program for this year's Causal Data Science Meeting (#CDSM2025) is now live!
📅 Nov 12–13, 2025 | 💻 Online | 🎟️ Free registration
Join us for two days of talks and debates at the intersection of causality, data science, and AI.
👉 causalscience.org
📅 Nov 12–13, 2025 | 💻 Online | 🎟️ Free registration
Join us for two days of talks and debates at the intersection of causality, data science, and AI.
👉 causalscience.org
Reposted by Raúl Peralta Lozada
🎥 The Wednesday conference talks are now live! ✨ Watch them now on our YouTube channel: www.youtube.com/@EuroPythonC...
October 20, 2025 at 12:52 PM
🎥 The Wednesday conference talks are now live! ✨ Watch them now on our YouTube channel: www.youtube.com/@EuroPythonC...
Reposted by Raúl Peralta Lozada
🚀 New talk!
"Automated ML-guided lead optimization: surpassing human-level performance at protein engineering"
▶️ www.youtube.com/watch?v=mEhB...
✨🧪 This was a talk I gave at the recent AIxBIO conference in Cambridge UK. A 10-minute pitch for what we do at Cradle!
"Automated ML-guided lead optimization: surpassing human-level performance at protein engineering"
▶️ www.youtube.com/watch?v=mEhB...
✨🧪 This was a talk I gave at the recent AIxBIO conference in Cambridge UK. A 10-minute pitch for what we do at Cradle!
Automated ML-guided lead optimization: surpassing human-level performance at protein engineering
YouTube video by Patrick Kidger
www.youtube.com
October 7, 2025 at 1:26 PM
🚀 New talk!
"Automated ML-guided lead optimization: surpassing human-level performance at protein engineering"
▶️ www.youtube.com/watch?v=mEhB...
✨🧪 This was a talk I gave at the recent AIxBIO conference in Cambridge UK. A 10-minute pitch for what we do at Cradle!
"Automated ML-guided lead optimization: surpassing human-level performance at protein engineering"
▶️ www.youtube.com/watch?v=mEhB...
✨🧪 This was a talk I gave at the recent AIxBIO conference in Cambridge UK. A 10-minute pitch for what we do at Cradle!
Reposted by Raúl Peralta Lozada
Reposted by Raúl Peralta Lozada
Whoa—my book is up for pre-order!
𝐌𝐨𝐝𝐞𝐥 𝐭𝐨 𝐌𝐞𝐚𝐧𝐢𝐧𝐠: 𝐇𝐨𝐰 𝐭𝐨 𝐈𝐧𝐭𝐞𝐫𝐩𝐫𝐞𝐭 𝐒𝐭𝐚𝐭 & 𝐌𝐋 𝐌𝐨𝐝𝐞𝐥𝐬 𝐢𝐧 #Rstats 𝐚𝐧𝐝 #PyData
The book presents an ultra-simple and powerful workflow to make sense of ± any model you fit
The web version will stay free forever and my proceeds go to charity.
tinyurl.com/4fk56fc8
𝐌𝐨𝐝𝐞𝐥 𝐭𝐨 𝐌𝐞𝐚𝐧𝐢𝐧𝐠: 𝐇𝐨𝐰 𝐭𝐨 𝐈𝐧𝐭𝐞𝐫𝐩𝐫𝐞𝐭 𝐒𝐭𝐚𝐭 & 𝐌𝐋 𝐌𝐨𝐝𝐞𝐥𝐬 𝐢𝐧 #Rstats 𝐚𝐧𝐝 #PyData
The book presents an ultra-simple and powerful workflow to make sense of ± any model you fit
The web version will stay free forever and my proceeds go to charity.
tinyurl.com/4fk56fc8
September 17, 2025 at 7:49 PM
Whoa—my book is up for pre-order!
𝐌𝐨𝐝𝐞𝐥 𝐭𝐨 𝐌𝐞𝐚𝐧𝐢𝐧𝐠: 𝐇𝐨𝐰 𝐭𝐨 𝐈𝐧𝐭𝐞𝐫𝐩𝐫𝐞𝐭 𝐒𝐭𝐚𝐭 & 𝐌𝐋 𝐌𝐨𝐝𝐞𝐥𝐬 𝐢𝐧 #Rstats 𝐚𝐧𝐝 #PyData
The book presents an ultra-simple and powerful workflow to make sense of ± any model you fit
The web version will stay free forever and my proceeds go to charity.
tinyurl.com/4fk56fc8
𝐌𝐨𝐝𝐞𝐥 𝐭𝐨 𝐌𝐞𝐚𝐧𝐢𝐧𝐠: 𝐇𝐨𝐰 𝐭𝐨 𝐈𝐧𝐭𝐞𝐫𝐩𝐫𝐞𝐭 𝐒𝐭𝐚𝐭 & 𝐌𝐋 𝐌𝐨𝐝𝐞𝐥𝐬 𝐢𝐧 #Rstats 𝐚𝐧𝐝 #PyData
The book presents an ultra-simple and powerful workflow to make sense of ± any model you fit
The web version will stay free forever and my proceeds go to charity.
tinyurl.com/4fk56fc8
Reposted by Raúl Peralta Lozada
A nice primer on normalizing flows by PyMC/PyTensor devs Ricardo and Jesse. pytensor.readthedocs.io/en/latest/ga...
Normalizing Flows in PyTensor — PyTensor dev documentation
pytensor.readthedocs.io
September 15, 2025 at 8:39 PM
A nice primer on normalizing flows by PyMC/PyTensor devs Ricardo and Jesse. pytensor.readthedocs.io/en/latest/ga...
Reposted by Raúl Peralta Lozada
PyMC people: Is there a way to implement a weighted formulation of a discrete count likelihood like the poisson, discrete weibull, etc? In Stan I'd typically do this via something like
for(n in 1:N)
target += ({function}(args...) * weights[n]);
for(n in 1:N)
target += ({function}(args...) * weights[n]);
September 12, 2025 at 9:06 PM
PyMC people: Is there a way to implement a weighted formulation of a discrete count likelihood like the poisson, discrete weibull, etc? In Stan I'd typically do this via something like
for(n in 1:N)
target += ({function}(args...) * weights[n]);
for(n in 1:N)
target += ({function}(args...) * weights[n]);
Reposted by Raúl Peralta Lozada
September 12, 2025 at 10:11 PM
Reposted by Raúl Peralta Lozada
Next up, this week's newsletter was about software interview questions! More precisely, how many of them are utterly trivialized by a half-decent constraint solver. buttondown.com/hillelwayne/...
Many Hard Leetcode Problems are Easy Constraint Problems
Use the right tool for the job.
buttondown.com
September 12, 2025 at 3:15 PM
Next up, this week's newsletter was about software interview questions! More precisely, how many of them are utterly trivialized by a half-decent constraint solver. buttondown.com/hillelwayne/...
Reposted by Raúl Peralta Lozada
At the risk of stretching the imagination beyond the limits of human endurance, the reader is asked to pretend that the author is interested in splines #rstats
Splines, B-splines, P-splines, and a disapproving kitten – Notes from a data witch
No, I do not care about splines. But I am trying to learn about GAMLSS regression, and yes, it is to this dark place that this topic has taken me
blog.djnavarro.net
September 8, 2025 at 1:41 AM
At the risk of stretching the imagination beyond the limits of human endurance, the reader is asked to pretend that the author is interested in splines #rstats
Reposted by Raúl Peralta Lozada
scikit-learn 1.8 will be the first scikit-learn release with native extensions that are officially marked as free-threading compatible.
github.com/scikit-learn...
github.com/scikit-learn...
MNT Mark cython extensions as free-threaded compatible by lesteve · Pull Request #31342 · scikit-learn/scikit-learn
Part of #30007
Cython 3.1 has been released on May 8 2025.
Following scipy PR scipy/scipy#22658 to use -Xfreethreading_compatible=True cython argument if cython >= 3.1
This cleans up the lock-fi...
github.com
September 2, 2025 at 4:51 PM
scikit-learn 1.8 will be the first scikit-learn release with native extensions that are officially marked as free-threading compatible.
github.com/scikit-learn...
github.com/scikit-learn...
Reposted by Raúl Peralta Lozada
Also in the works, but every prediction and aggregation function needs to be written manually from scratch, so it's a big project. On big datasets, it can be crazy fast. see benchmarks. github.com/arcruz0/marg...
GitHub - arcruz0/marginaleffectsJAX: A JAX Backend for `marginaleffects`
A JAX Backend for `marginaleffects`. Contribute to arcruz0/marginaleffectsJAX development by creating an account on GitHub.
github.com
August 26, 2025 at 3:42 PM
Also in the works, but every prediction and aggregation function needs to be written manually from scratch, so it's a big project. On big datasets, it can be crazy fast. see benchmarks. github.com/arcruz0/marg...
Reposted by Raúl Peralta Lozada
✨🐼 Pandas 3.0 will have `pd.col` syntax
💡 You can use it for clean chaining in `assign` and `loc`
🚀 It's happening, the PR just got merged!
💡 You can use it for clean chaining in `assign` and `loc`
🚀 It's happening, the PR just got merged!
August 22, 2025 at 5:28 PM
✨🐼 Pandas 3.0 will have `pd.col` syntax
💡 You can use it for clean chaining in `assign` and `loc`
🚀 It's happening, the PR just got merged!
💡 You can use it for clean chaining in `assign` and `loc`
🚀 It's happening, the PR just got merged!
Reposted by Raúl Peralta Lozada
Our didactic review on machine learning for causal inference, now open access:
• identifiability (theory of when the data can answer a causal question)
• machine-learning estimators
• study design (asking well-framed questions + loopholes, eg with timewise data)
www.annualreviews.org/content/jour...
• identifiability (theory of when the data can answer a causal question)
• machine-learning estimators
• study design (asking well-framed questions + loopholes, eg with timewise data)
www.annualreviews.org/content/jour...
August 20, 2025 at 7:12 PM
Our didactic review on machine learning for causal inference, now open access:
• identifiability (theory of when the data can answer a causal question)
• machine-learning estimators
• study design (asking well-framed questions + loopholes, eg with timewise data)
www.annualreviews.org/content/jour...
• identifiability (theory of when the data can answer a causal question)
• machine-learning estimators
• study design (asking well-framed questions + loopholes, eg with timewise data)
www.annualreviews.org/content/jour...
Reposted by Raúl Peralta Lozada
Looky Looky! 😍🥳👏
arxiv.org/abs/2508.12939
Super fun project, I ❤️ed coauthoring w/ @sbi-devs.bsky.social.
Great lead by @deismic.bsky.social & @janboelts.bsky.social. Contribs by many talented people @jakhmack.bsky.social. 🙏 to #BenjaminKurtMiller for the kickstart! @helmholtzai.bsky.social
arxiv.org/abs/2508.12939
Super fun project, I ❤️ed coauthoring w/ @sbi-devs.bsky.social.
Great lead by @deismic.bsky.social & @janboelts.bsky.social. Contribs by many talented people @jakhmack.bsky.social. 🙏 to #BenjaminKurtMiller for the kickstart! @helmholtzai.bsky.social
Simulation-Based Inference: A Practical Guide
A central challenge in many areas of science and engineering is to identify model parameters that are consistent with prior knowledge and empirical data. Bayesian inference offers a principled framewo...
arxiv.org
August 19, 2025 at 7:32 AM
Looky Looky! 😍🥳👏
arxiv.org/abs/2508.12939
Super fun project, I ❤️ed coauthoring w/ @sbi-devs.bsky.social.
Great lead by @deismic.bsky.social & @janboelts.bsky.social. Contribs by many talented people @jakhmack.bsky.social. 🙏 to #BenjaminKurtMiller for the kickstart! @helmholtzai.bsky.social
arxiv.org/abs/2508.12939
Super fun project, I ❤️ed coauthoring w/ @sbi-devs.bsky.social.
Great lead by @deismic.bsky.social & @janboelts.bsky.social. Contribs by many talented people @jakhmack.bsky.social. 🙏 to #BenjaminKurtMiller for the kickstart! @helmholtzai.bsky.social
Reposted by Raúl Peralta Lozada
In about an hour, I will be presenting an introductory talk on PyMC for new users. I try to address common problems and introduce the library in a non-technical way (pictures > equations). Feel free to join!
www.meetup.com/data-umbrell...
www.meetup.com/data-umbrell...
[Online] A Tutorial for Getting Started with PyMC, Tue, Aug 12, 2025, 12:00 PM | Meetup
This one-hour tutorial introduces new users to version 5 of PyMC, a powerful Python, open source library for probabilistic programming and Bayesian statistical modeling. Pa
www.meetup.com
August 12, 2025 at 2:20 PM
In about an hour, I will be presenting an introductory talk on PyMC for new users. I try to address common problems and introduce the library in a non-technical way (pictures > equations). Feel free to join!
www.meetup.com/data-umbrell...
www.meetup.com/data-umbrell...
Reposted by Raúl Peralta Lozada
Dive into our SciPy 2025 talk on Pixi Build now! Watch here: www.youtube.com/watch?v=Uey...
Ruben Arts & Wolf Vollprecht - Reproducible Science Made Easy: Package Management with Pixi
Reproducibility is a major underpinning of the scientific method. In scientific computing, this also includes the ability to reproduce your dependencies. Yet...
www.youtube.com
August 12, 2025 at 9:59 AM
Dive into our SciPy 2025 talk on Pixi Build now! Watch here: www.youtube.com/watch?v=Uey...
Reposted by Raúl Peralta Lozada
Reminder that all three books I've co-authored are freely available online for non-commercial use (and the fourth will be, too)
All three books I've co-authored are freely available online for non-commercial use:
- #Bayesian Data Analysis, 3rd ed (aka BDA3) at stat.columbia.edu/~gelman/book/
- #Regression and Other Stories at avehtari.github.io/ROS-Examples/
- Active Statistics at avehtari.github.io/ActiveStatis...
- #Bayesian Data Analysis, 3rd ed (aka BDA3) at stat.columbia.edu/~gelman/book/
- #Regression and Other Stories at avehtari.github.io/ROS-Examples/
- Active Statistics at avehtari.github.io/ActiveStatis...
August 11, 2025 at 5:44 PM
Reminder that all three books I've co-authored are freely available online for non-commercial use (and the fourth will be, too)
Reposted by Raúl Peralta Lozada
Tamara Kolda has a new book out: "Tensor Decompositions for Data Science:" www.mathsci.ai/post/tensor-.... Looks cool! There's a free pdf at the link.
ToC (abbreviated).
#statsky #mathsky
ToC (abbreviated).
#statsky #mathsky
August 11, 2025 at 1:44 AM
Tamara Kolda has a new book out: "Tensor Decompositions for Data Science:" www.mathsci.ai/post/tensor-.... Looks cool! There's a free pdf at the link.
ToC (abbreviated).
#statsky #mathsky
ToC (abbreviated).
#statsky #mathsky
Reposted by Raúl Peralta Lozada
learning a new api? `mo.inspect()` your objects in
@marimo.io. no more dir() / help()-ing around `
@marimo.io. no more dir() / help()-ing around `
August 6, 2025 at 6:07 PM
learning a new api? `mo.inspect()` your objects in
@marimo.io. no more dir() / help()-ing around `
@marimo.io. no more dir() / help()-ing around `