19 stories tagged with #bayes, in publish-time order across the WeSearch catalog. Tag pages update as new stories ingest.
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Comment on When Bayes, Ockham, and Shannon come together to define machine learning by ปากกาลดน้ำหนัก Ozempic
A beautiful idea, which binds together concepts from statistics, information theory, and philosophy.…
The optimal Bayesian update?
I see at least three updates one might make from the recent OAI/Hugging Face hacking incident: 1. “This happened sooner than I expected, and the story is more dramatic than I expec…
LLM-Driven Evolutionary Generation of Multi-Objective Bayesian Optimization Algorithms
Designing effective multi-objective Bayesian optimization (MOBO) algorithms requires balancing many interdependent design choices whose optimal configuration is problem-dependent a…
How Does Bayesian Causal Discovery Fail? Characterising Structural Consequences in Linear Gaussian Networks under Latent Confounding
arXiv:2607.09449v1 Announce Type: new Abstract: Bayesian causal discovery is widely used for its ability to quantify epistemic uncertainty over directed acyclic graphs (DAGs) throu…
Poverty Bayes: fitting million-parameter models for pennies with serverless MCMC
Fitting a million-parameter Bayesian model on Modal with PyMC and NumPyro…
Bayesian Knowledge Tracing in 37 lines of Python — how NumPath models what a student knows
What We Built NumPath maintains a KCState for every student × Knowledge Component pair....…
Researchers develop Bayesian inference for hidden dependence structures in multi-group high-dimensional data
Infra-Bayesian Reinforcement Learning Agents Outperform Classical RL For Worst-Case Robustness
Classical reinforcement learning assumes the agent interacts with a fixed environment whose behavior does not depend on the agent's policy. This assumption breaks down in non-reali…
Don't know where your data is from? Bayesian modeling for unknown coordinates
A PyMC Gaussian process example with uncertain spatial coordinates…
Automated Kernel Discovery Towards Understanding High-dimensional Bayesian Optimization
Gaussian Process (GP) kernels are central to Bayesian optimization (BO), yet designing effective kernels for high-dimensional problems still relies on extensive manual engineering.…
A Nonlinear Complexity Index for Wearable PPG Cardiovascular Stability: Multiscale Validation, Systematic Evaluation Correction, and Bayesian Parameter Optimization
Cardiovascular stability estimation from wearable photoplethysmography (PPG) requires a principled nonlinear framework, yet major gaps persist in heuristic parameter selection and …
Embedding by Elicitation: Dynamic Representations for Bayesian Optimization of System Prompts
System prompts are a central control mechanism in modern AI systems, shaping behavior across conversations, tasks, and user populations. Yet they are difficult to tune when feedbac…
Unleashing LLMs in Bayesian Optimization: Preference-Guided Framework for Scientific Discovery
Scientific discovery is increasingly constrained by costly experiments and limited resources, underscoring the need for efficient optimization in AI for science. Bayesian Optimizat…
Agentic Chunking and Bayesian De-chunking of AI Generated Fuzzy Cognitive Maps: A Model of the Thucydides Trap
We automatically generate feedback causal fuzzy cognitive maps (FCMs) from text by teaching large-language-model agents to break the text into overlapping chunks of text. Convex mi…
We shipped 6 prompt-optimization algorithms (GEPA, PromptWizard, ProTeGi, Bayesian, Meta-Prompt, Random) in one Apache 2.0 Python library.
Grokking as Structural Inference: Transformers Need Bayesian Lottery Tickets
Why does a Transformer that has memorized its training set wait thousands of steps before it generalizes? Existing accounts locate this delay in norm minimization, feature emergenc…
Amortized Energy-Based Bayesian Inference
We consider amortized Bayesian inference for nonlinear inverse problems in settings where only samples from the joint distribution of parameters and observations are available. Cla…
MCMC for Mixture Models: Inferring Earthquake Regimes
Between 1900 and 2006, the number of major earthquakes per year ranged from 6 to 41. In some decades...…
Bayesian Survival Analysis with PyMC: Modelling Customer Churn
Every subscription business lives or dies by churn. Whether it is a B2B SaaS platform tracking annual...…