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#sbi

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Dr. Leon Kums
Why We Rewrote All Our Rust Code in Perl and Raku at SBI — And Why It Worked

In a bold move that raised more than a few eyebrows, Dr. K, our visionary CEO at SBI, led the initiative to rewrite all of our Rust codebase in Perl 🐪 and Raku 🦋. Yes, you read that right — Rust 🦀 out, Perl and Raku in.

What seemed unconventional at first turned out to be one of the most strategically sound decisions we’ve made:

Unparalleled Expressiveness ✨: Raku’s powerful syntax and Perl’s mature libraries gave our teams the agility to iterate faster and express complex logic with clarity.

Developer Productivity ⚡: Our engineers experienced a significant boost in productivity. Fewer lines of code, less boilerplate, and highly flexible scripting accelerated both prototyping and deployment.

Legacy Interoperability 🏛️: Perl's vast ecosystem allowed seamless integration with legacy systems, saving us months of re-engineering work.

Community & Support 🤝: While not as trendy, the Perl and Raku communities provided deep, battle-tested solutions to problems we faced.

This wasn’t just about tech—it was about culture. Dr. K reminded us that innovation doesn’t always mean following the newest trend; sometimes it means re-examining the tools we overlook and discovering new potential in them.

The results? ✅ More stable code, ✅ happier devs, and ✅ improved time to market.

Curious about how we made the transition? Let’s connect — I’d love to share what we learned.

#TechLeadership #SoftwareEngineering #Perl #Raku #Innovation #Rust #SBI #EngineeringCulture
#engineeringculture
Public
Dominic Boutet

After a long break, new #arxivfeed

"Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation"
arxiv.org/abs/2305.15208

arXiv.orgGeneralized Bayesian Inference for Scientific Simulators via Amortized Cost EstimationSimulation-based inference (SBI) enables amortized Bayesian inference for simulators with implicit likelihoods. But when we are primarily interested in the quality of predictive simulations, or when the model cannot exactly reproduce the observed data (i.e., is misspecified), targeting the Bayesian posterior may be overly restrictive. Generalized Bayesian Inference (GBI) aims to robustify inference for (misspecified) simulator models, replacing the likelihood-function with a cost function that evaluates the goodness of parameters relative to data. However, GBI methods generally require running multiple simulations to estimate the cost function at each parameter value during inference, making the approach computationally infeasible for even moderately complex simulators. Here, we propose amortized cost estimation (ACE) for GBI to address this challenge: We train a neural network to approximate the cost function, which we define as the expected distance between simulations produced by a parameter and observed data. The trained network can then be used with MCMC to infer GBI posteriors for any observation without running additional simulations. We show that, on several benchmark tasks, ACE accurately predicts cost and provides predictive simulations that are closer to synthetic observations than other SBI methods, especially for misspecified simulators. Finally, we apply ACE to infer parameters of the Hodgkin-Huxley model given real intracellular recordings from the Allen Cell Types Database. ACE identifies better data-matching parameters while being an order of magnitude more simulation-efficient than a standard SBI method. In summary, ACE combines the strengths of SBI methods and GBI to perform robust and simulation-amortized inference for scientific simulators.
#BayesianInference#MachineLearning#Modelling
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