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Tony Seba’s Prediction: Nuclear Obsolete by 2030 — Wind, Solar, and Battery Storage the Future

Small modular nuclear reactors are too expensive, too slow, and too risky, and the focus should be on wind, solar, and battery storage for energy needs Questions to inspire discussion What did Tony Seba predict about nuclear power in 2014? —Tony Seba predicted in 2014 that nuclear power would be obsolete by 2030, and recent research has shown that his predictions about the cost blowouts and inefficiency of small modular nuclear reactors were accurate.

The Order of Time: Carlo Rovelli explains that time doesn’t really exist

The bestselling author of Seven Brief Lessons on Physics introduces the mysteries of time, further explored in his new book, The Order of Time.

Time is a mystery that does not cease to puzzle us. Philosophers, artists and poets have long explored its meaning while scientists have found that its structure is different from the simple idea we have of it. From Einstein to quantum theory and beyond, our understanding of time has been undergoing radical transformations. Time flows at a different speed in different places, the past and the future differ far less than we might think, and the very notion of the present evaporates in the vast universe.

The Order of Time will be available in hardback and in audiobook read by Benedict Cumberbatch on April 26th 2018
Pre-order now: https://amzn.to/2vaq6cw.

View a trailer for the book, featuring Benedict Cumberbatch: • Benedict Cumberbatch on The Order of…

Director: ed fenwick. interviewer: sam voulters

Tutorial | Bayesian causal inference: A critical review and tutorial (Standard Format)

This tutorial aims to provide a survey of the Bayesian perspective of causal inference under the potential outcomes framework. We review the causal estimands, assignment mechanism, the general structure of Bayesian inference of causal effects, and sensitivity analysis. We highlight issues that are unique to Bayesian causal inference, including the role of the propensity score, the definition of identifiability, the choice of priors in both low and high dimensional regimes. We point out the central role of covariate overlap and more generally the design stage in Bayesian causal inference. We extend the discussion to two complex assignment mechanisms: instrumental variable and time-varying treatments. We identify the strengths and weaknesses of the Bayesian approach to causal inference. Throughout, we illustrate the key concepts via examples.

Instructor:

Fan Li, Professor, Department of Statistical Science, Department of Biostatistics \& Bioinformatics, Duke University.

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