Bayesian Statistics

Beth Gardner introduces spatial capture-recapture models for estimating wildlife population dynamics and movement. The seminar considers observations from camera traps and genetic identification using hair or scat, showing how the modeling framework can accommodate different sampling methods, time scales and movement processes. Gardner develops the Bayesian formulation through worked examples in R with NIMBLE, then discusses extensions and audience questions. The methods support estimates of demographic rates, spatial distributions and habitat relationships for conservation and management. The organizer explicitly schedules this occurrence at 15:00 Eastern time, rather than the series’ usual time.

Raymundo Arróyave discusses alloy discovery guided by both metallurgical knowledge and Bayesian optimization. The approach combines physical models, high-throughput CALPHAD calculations, microstructural information and experimental feedback to search chemical and processing spaces that are difficult to explore through data alone. Examples involving high-entropy and refractory alloys address multiple objectives and constraints, including feasibility, correlated properties, uncertainty and microstructural sensitivity. The seminar then connects these decision methods to the Autonomous Robotic Metallurgist under development at his university. This platform links modular synthesis, characterization and testing with digital twins, human–robot collaboration and AI agents. The aim is a closed experimental loop in which scientific judgment and physical understanding guide automated execution.

To act effectively and flexibly in an imperfectly predictable environment with only incomplete and unreliable sensory information, animals must learn to form and compute with internal representations that reflect their necessarily uncertain beliefs about the state of the world. The optimal approach to handling uncertainty is rooted in Bayesian probability, and indeed humans and other animals often approach Bayes optimality with a degree of robustness and flexibility that continues to evade artificial systems. However, the question of how neural circuits organise to achieve this performance remains one of the fundamental mysteries of neuroscience. I will discuss a series of models built around the idea that distributional information is naturally encoded in a distributed fashion by neural population firing rates that converge on the mean values of non-linear functions of state. We will see that such representations emerge naturally in task-optimised systems, and also provide a simple and effective substrate for unsupervised learning. Finally, I will sketch ongoing work that links the emergence of such representations to the architecture of recurrent neural circuits. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2021-07-07. Recording duration: 00:52:28.

The University of Cambridge's Handley Lab seeks a researcher to connect fundamental theories with cosmological and astrophysical observations using AI-assisted scientific software and GPU-accelerated inference. The role includes developing physical predictions, Bayesian methods including nested sampling, publishing research and shaping an independent research programme. The post is based at the Kavli Institute for Cosmology and Institute of Astronomy, with funding initially available until 30 September 2030. Appointment is at Grade 7 or Grade 9 according to experience; Grade 9 requires Faculty Board approval. Salary: £37,694–£59,966. Applications close on 25 September 2026 at 23:59 GMT, as stated by the employer. Submit a cover letter, CV, publications, research statement, evidence of computational skills and two academic referee contacts through the university recruitment system. A PhD, completed or near completion, in physics, astronomy, applied mathematics or a related field is required. Reference LG51052.

The University of Cambridge's Handley Lab seeks a researcher to connect fundamental theories with cosmological and astrophysical observations using AI-assisted scientific software and GPU-accelerated inference. The role includes developing physical predictions, Bayesian methods including nested sampling, publishing research and shaping an independent research programme. The post is based at the Kavli Institute for Cosmology and Institute of Astronomy, with funding initially available until 30 September 2030. Appointment is at Grade 7 or Grade 9 according to experience; Grade 9 requires Faculty Board approval. Salary: £37,694–£59,966. Applications close on 25 September 2026 at 23:59 GMT, as stated by the employer. Submit a cover letter, CV, publications, research statement, evidence of computational skills and two academic referee contacts through the university recruitment system. A PhD, completed or near completion, in physics, astronomy, applied mathematics or a related field is required. Reference LG51052.

Raymundo Arróyave discusses alloy discovery guided by both metallurgical knowledge and Bayesian optimization. The approach combines physical models, high-throughput CALPHAD calculations, microstructural information and experimental feedback to search chemical and processing spaces that are difficult to explore through data alone. Examples involving high-entropy and refractory alloys address multiple objectives and constraints, including feasibility, correlated properties, uncertainty and microstructural sensitivity. The seminar then connects these decision methods to the Autonomous Robotic Metallurgist under development at his university. This platform links modular synthesis, characterization and testing with digital twins, human–robot collaboration and AI agents. The aim is a closed experimental loop in which scientific judgment and physical understanding guide automated execution.

Beth Gardner introduces spatial capture-recapture models for estimating wildlife population dynamics and movement. The seminar considers observations from camera traps and genetic identification using hair or scat, showing how the modeling framework can accommodate different sampling methods, time scales and movement processes. Gardner develops the Bayesian formulation through worked examples in R with NIMBLE, then discusses extensions and audience questions. The methods support estimates of demographic rates, spatial distributions and habitat relationships for conservation and management. The organizer explicitly schedules this occurrence at 15:00 Eastern time, rather than the series’ usual time.