I like studying statistical problems.
Joined September 2019
- Tweets 66
- Following 167
- Followers 165
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6 Photos and videos
12 Nov 2024
Very excited to give the next BNP webinar!
I will be talking about some recent works on feature allocation models and several generalizations 🎉
12 Nov 2024
Next webinar organised by BNP-ISBA will be
DATE & TIME: 17:00 UTC on December 4th, 2024.
SPEAKER: Mario Beraha (Polytechnic of Milan)
TITLE: Bayesian analysis of (extended) feature allocation models: predictions, sufficientness, and applications
bnp-isba.github.io/webinars.…
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Mario Beraha retweeted
9 Nov 2024
📢I’m really proud of “Bayesian clustering of high-dimensional data via latent repulsive mixtures’’ just appeared on Biometrika, Advance articles, doi.org/10.1093/biomet/asae0…. Thank you to my terrific coauthors Lorenzo Ghilotti and @mberaha2.
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Mario Beraha retweeted
9 Nov 2024
📢The submission of contributed talks for the 14th International Conference on Bayesian Nonparametrics, UCLA (Los Angeles, US), June 23-27, 2025, is now OPEN! Deadline for submission: Dec 15, 2024.
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Mario Beraha retweeted
25 Mar 2024
Average temperatures by year, with the years listed in alphabetical order, and suddenly the climate crisis looks like a joke.
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Mario Beraha retweeted
16 Nov 2023
Warning: extremely difficult text to read below.
Last night I surrendered my phone and signed a waiver. And then I sat with a small group and an Israeli military attaché and we watched the 45 minute compilation of Hamas videos from October 7th.
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28 Sep 2023
Together with Stefano Favaro and Matteo Sesia, we just arXiv'd our latest take on frequency and cardinality estimation from compressed (sketched) data:
arxiv.org/abs/2309.15408
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28 Sep 2023
Hence, we adopt a pragmatic approach and propose the class "smoothed" estimators, which work very well in practice and are easy to compute! We extend the analysis to sketches obtained with multiple hash functions by drawing from the "multi-view" literature.
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28 Sep 2023
As a bonus, we show that our model-based approach can be used to infer the cardinality of the dataset as well. This is another classical problem in computer science which was typically solved using a different data structure!
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28 Sep 2023
Hence, we adopt a pragmatic approach and propose the class "smoothed" estimators, which work very well in practice and are easy to compute! We extend the analysis to sketches obtained with multiple hash functions by drawing from the "multi-view" literature.
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28 Sep 2023
As a bonus, we show that our model-based approach can be used to infer the cardinality of the dataset as well. This is another classical problem in computer science which was typically solved using a different data structure!
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Mario Beraha retweeted
22 Sep 2023
@mberaha2 and I have just published the first project we started collaborating 5 years ago! Meanwhile we have taken onboard 4 more coauthors, and I thank them all. See Childhood obesity in Singapore: A Bayesian nonparametric approach, SMJ OnlineFirst
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5 Apr 2023
Personal update. As of last week, I'm officially a Ph.D. in data science and computation. My thesis was on the statistical learning of RPMs, under the supervision of @AlessandraGugl9!
We also arXived the first two papers from my postdoc, joint works with Stefano Favaro.
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5 Apr 2023
Indeed, under a CRM prior, the predictive distribution of the number of "new" traits in an additional sample depends only on the sample size!
We propose a new class of priors derived from the scaled subordinators by James et al. (2015).
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5 Apr 2023
In particular, we show that, in some cases, this leads to a predictive distribution that depends on the sample size and the number of unique traits in the sample, similarly to what happens under the Pitman-Yor process prior!
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