Generalised linear mixed model analysis via sequential Monte Carlo sampling

We present a sequential Monte Carlo sampler algorithm for the Bayesian analysis of generalised linear mixed models (GLMMs). These models support a variety of interesting regression-type analyses, but performing inference is often extremely difficult, even when using the Bayesian approach combined with Markov chainMonte Carlo (MCMC). The SequentialMonte Carlo sampler (SMC) is a new and generalmethod for producing samples from posterior distributions. In thisarticle we demonstrate use of the SMC method for performing inference for GLMMs. We demonstrate the effectiveness of the method on both simulated and real data, and find that sequential Monte Carlo is a competitive alternative to the available MCMC techniques. © 2008, Institute of Mathematical Statistics. All rights reserved

Reactive probabilistic programming

International audienceSynchronous modeling is at the heart of programming languages like Lustre, Esterel, or SCADE used routinely for implementing safety critical control software, e.g., fly-bywire and engine control in planes. However, to date these languages have had limited modern support for modeling uncertainty-probabilistic aspects of the software's environment or behavior-even though modeling uncertainty is a primary activity when designing a control system. In this paper we present ProbZelus the first synchronous probabilistic programming language. ProbZelus conservatively provides the facilities of a synchronous language to write control software, with probabilistic constructs to model uncertainties and perform inference-in-the-loop. We present the design and implementation of the language. We propose a measure-theoretic semantics of probabilistic stream functions and a simple type discipline to separate deterministic and probabilistic expressions. We demonstrate a semantics-preserving compilation into a first-order functional language that lends itself to a simple presentation of inference algorithms for streaming models. We also redesign the delayed sampling inference algorithm to provide efficient streaming inference. Together with an evaluation on several reactive applications, our results demonstrate that ProbZelus enables the design of reactive probabilistic applications and efficient, bounded memory inference

What's soil got to do with climate change?

This comic is based on Prof. Asmeret Asefaw Berhe's TEDtalk "A climate change solution that's right under our feet" (https://www.ted.com/talks/asmeret_asefaw_berhe_a_climate_change_solution_that_s_right_under_our_feet?language=en) Abstract from TED.com: There's two times more carbon in the earth's soil than in all of its vegetation and the atmosphere -- combined. Biogeochemist Asmeret Asefaw Berhe dives into the science of soil and shares how we could use its awesome carbon-trapping power to offset climate change. "[Soil] represents the difference between life and lifelessness in the earth system, and it can also help us combat climate change -- if we can only stop treating it like dirt," she says