We harness the power of Bayesian emulation techniques, designed to aid the analysis of complex computer models, to examine the structure of complex Bayesian analyses themselves. These techniques facilitate robust Bayesian analyses and/or sensitivity analyses of complex problems, and hence allow global exploration of the impacts of choices made in both the likelihood and prior specification. We show how previously intractable problems in robustness studies can be overcome using emulation techniques, and how these methods allow other scientists to quickly extract approximations to posterior results corresponding to their own particular subjective specification. The utility and flexibility of our method is demonstrated on a reanalysis of a real application where Bayesian methods were employed to capture beliefs about river flow. We discuss the obvious extensions and directions of future research that such an approach opens up.

Increasingly complex generative models are being used across disciplines as they allow for realistic characterization of data, but a common difficulty with them is the prohibitively large computational cost to evaluate the likelihood function and thus to perform likelihood-based statistical inference. A likelihood-free inference framework has emerged where the parameters are identified by finding values that yield simulated data resembling the observed data. While widely applicable, a major difficulty in this framework is how to measure the discrepancy between the simulated and observed data. Transforming the original problem into a problem of classifying the data into simulated versus observed, we find that classification accuracy can be used to assess the discrepancy. The complete arsenal of classification methods becomes thereby available for inference of intractable generative models. We validate our approach using theory and simulations for both point estimation and Bayesian inference, and demonstrate its use on real data by inferring an individual-based epidemiological model for bacterial infections in child care centers.

This note is concerned with accurate and computationally efficient approximations of moments of Gaussian random variables passed through sigmoid or softmax mappings. These approximations are semi-analytical (i.e. they involve the numerical adjustment of parametric forms) and highly accurate (they yield 5% error at most). We also highlight a few niche applications of these approximations, which arise in the context of, e.g., drift-diffusion models of decision making or non-parametric data clustering approaches. We provide these as examples of efficient alternatives to more tedious derivations that would be needed if one was to approach the underlying mathematical issues in a more formal way. We hope that this technical note will be helpful to modellers facing similar mathematical issues, although maybe stemming from different academic prospects.

We introduce Deep Variational Bayes Filters (DVBF), a new method for unsupervised learning and identification of latent Markovian state space models. Leveraging recent advances in Stochastic Gradient Variational Bayes, DVBF can overcome intractable inference distributions via variational inference. Thus, it can handle highly nonlinear input data with temporal and spatial dependencies such as image sequences without domain knowledge. Our experiments show that enabling backpropagation through transitions enforces state space assumptions and significantly improves information content of the latent embedding. This also enables realistic long-term prediction.

March 2, 2017 --Computer engineers have created some amazingly small devices, capable of storing entire libraries of music and movies in the palm of your hand. But geneticists say Mother Nature can do even better. DNA, where all of biology's information is stored, is incredibly dense. The whole genome of an organism fits into a cell that is invisible to the naked eye. That's why computer scientists are turning to microbiology to design the next best way to store humanity's ever-increasing collection of digital data.

WHEN someone goes into cardiac arrest, survival depends on how quickly the heart can be restarted. Enter Amazon's Echo, a voice-driven computer that answers to the name of Alexa, which can recite life-saving instructions about cardiopulmonary resuscitation, a skill taught to it by the American Heart Association. Alexa is accumulating other health-care skills, too, including acting as a companion for the elderly and answering questions about children's illnesses. In the near future she will probably help doctors with grubby hands to take notes and to request scans, as well as remind patients to take their pills. Alexa is one manifestation of a drive to disrupt an industry that has so far largely failed to deliver on the potential of digital information.

If you want to know the future of business consulting, just look to the firms and universities at the forefront of a revolution in technology and a transformation in recruiting. I write those words as both a means of advice, to readers, and as an admonition, to companies. Far too many businesses are not ready for this change -- too few are even aware that change is afoot -- so preparing for this event is critical to the health of the economy and the expansion of the workforce as a whole. I refer, specifically, to the rise of artificial intelligence (A.I.) and the proliferation of big data. According to Braden Kelley, an innovation and consulting expert, a good way to understand this shift is to review the key concepts presented in this image of The Knowledge Funnel, a concept Roger Martin introduced in his book The Design of Business.

Beginning today, Google Assistant is opening up to new devices around the world. Both Nougat and Marshmallow-powered phones are gaining access to the artificial intelligence platform. Google is making the feature live in select countries to start, so you may not get to use Google Assistant for a couple of days, weeks, and or even months. The first set of people get it are English-speaking users in the United States. After that, English-speaking users in Australia will be next.

Do students learn as much when they read digitally as they do in print? For both parents and teachers, knowing whether computer-based media are improving or compromising education is a question of concern. With the surge in popularity of e-books, online learning and open educational resources, investigators have been trying to determine whether students do as well when reading an assigned text on a digital screen as on paper. The answer to the question, however, needs far more than a yes-no response. In my research, I have compared the ways in which we read in print and onscreen.

A worker picks substrate-grown strawberries at the Driscoll's Inc. facility on the McGrath Ranch in Watsonville, Calif., on Sept. 19, 2016. Buoyed by an inexpensive migrant workforce, California has been the United States' agricultural mainstay for nearly a century, currently producing about 60 percent of the nation's fresh produce. But as the state's minimum wage approaches $15 an hour and competition from a growing Mexican economy mounts, producers face unprecedented operating costs and a workforce that has dropped by 60 percent since the 1990s. Add to this President Trump's moves to restrict immigration, which threatens to significantly curtail the sector's already depressed labor supply. Leading California-based growers like Driscoll's Berries and Taylor Farms are feeling the immediacy of Trump's executive orders, as millions of dollars of specialty crops are growing right now that will require a workforce to pick them at the end of the season. Together they spend over a billion dollars on labor each year.