Gaussian graphical model is a graphical representation of the dependence structure for a Gaussian random vector. It is recognized as a powerful tool in different applied fields such as bioinformatics, error-control codes, speech language, information retrieval and others. Gaussian graphical model selection is a statistical problem to identify the Gaussian graphical model from a sample of a given size. Different approaches for Gaussian graphical model selection are suggested in the literature. One of them is based on considering the family of individual conditional independence tests. The application of this approach leads to the construction of a variety of multiple testing statistical procedures for Gaussian graphical model selection. An important characteristic of these procedures is its error rate for a given sample size. In existing literature great attention is paid to the control of error rates for incorrect edge inclusion (Type I error). However, in graphical model selection it is also important to take into account error rates for incorrect edge exclusion (Type II error). To deal with this issue we consider the graphical model selection problem in the framework of the multiple decision theory. The quality of statistical procedures is measured by a risk function with additive losses. Additive losses allow both types of errors to be taken into account. We construct the tests of a Neyman structure for individual hypotheses and combine them to obtain a multiple decision statistical procedure. We show that the obtained procedure is optimal in the sense that it minimizes the linear combination of expected numbers of Type I and Type II errors in the class of unbiased multiple decision procedures.

For example, genes may regulate each other in different ways when external conditions are changed; the number of daily flu-like symptom reports in nearby hospitals may become correlated when a major epidemic disease breaks out; EEG signals from different regions of the brain may be synchronized/desynchronized when the subject is performing different activities. Spotting such changes in interactions may provide key insights into the underlying system. The interactions among random variables can be formulated as undirected probabilistic graphical models, or Markov Networks (MNs) [Koller and Friedman, 2009], expressing the interactions via the conditional independence. We consider a simple model: the pairwise MNs where the links are only encoded for single or pairs of random variables. Due to the Hammersley-Clifford theorem [Hammersley and Clifford, 1971], the underlying joint probability density function can be represented as the product of univariate and bivariate factors.

In this paper, we address the fundamental problem of line spectral estimation in a Bayesian framework. We target model order and parameter estimation via variational inference in a probabilistic model in which the frequencies are continuous-valued, i.e., not restricted to a grid; and the coefficients are governed by a Bernoulli-Gaussian prior model turning model order selection into binary sequence detection. Unlike earlier works which retain only point estimates of the frequencies, we undertake a more complete Bayesian treatment by estimating the posterior probability density functions (pdfs) of the frequencies and computing expectations over them. Thus, we additionally capture and operate with the uncertainty of the frequency estimates. Aiming to maximize the model evidence, variational optimization provides analytic approximations of the posterior pdfs and also gives estimates of the additional parameters. We propose an accurate representation of the pdfs of the frequencies by mixtures of von Mises pdfs, which yields closed-form expectations. We define the algorithm VALSE in which the estimates of the pdfs and parameters are iteratively updated. VALSE is a gridless, convergent method, does not require parameter tuning, can easily include prior knowledge about the frequencies and provides approximate posterior pdfs based on which the uncertainty in line spectral estimation can be quantified. Simulation results show that accounting for the uncertainty of frequency estimates, rather than computing just point estimates, significantly improves the performance. The performance of VALSE is superior to that of state-of-the-art methods and closely approaches the Cram\'er-Rao bound computed for the true model order.

In August 2016, Econsultancy published a report in association with IBM called The Secrets of Elite Analytics Practices. Part of this wide ranging report seeks to discover just how automation and AI have changed analytics in marketing. Time was identified as a business's most precious resource. Being able to streamline the marketing function through automation and, in particular, the analytics portion, was something executives deemed hugely valuable. But is automation driving out innovation and originality?

Fukoku Mutual Life Insurance, seeking greater efficiency in calculating their payouts to policyholders, will soon replace many of its office workers with an AI system based on IBM's Watson Explorer ("a cognitive technology that can think like a human"). In a recent press release, Fukoku Mutual Life Insurance stated an expected increase in productivity by 30% from their "Diagnostic document assessment automatic coding system." The AI system will be used to read and understand medical certificates, hospital stays, surgical procedures, and medical history to make a more accurate assessment of payouts. According to reporting from the Guardian, the company will see a return on investment in less than two years. In a small concession for human workers, the payouts will not be finalized until approved by a non-AI staffer.

IBM (NYSE:IBM) jointly announced with the German conglomerate Siemens (OTCPK:SIEGY) that they are planning to include IBM's Watson in Siemens's industry analytics platform MindSphere. Siemens is Europe's largest manufacturing and electronics company with a worldwide presence. Siemens operates in the industrial sector with lots of on-premises software suites, which the company is willing to send to cloud. As a result, IBM's Watson will get a significant boost. This article investigates how IBM's Watson platform will benefit from the development.

To process an image, for example, the lowest layer is fed the raw images. It notes things like the brightness and colours of individual pixels, and how those properties are distributed across the image. The next layer combines these observations into more abstract categories, identifying edges, shadows and the like. The layer after that will analyse those edges and shadows in turn, looking for combinations that signify features such as eyes, lips and ears. And these can then be combined into a representation of a face--and indeed not just any face, but even a new image of a particular face that the network has seen before.

After years of anticipation, Faraday Future unveiled its "Tesla-killer" FF91 electric car this week -- and it's even faster than a Model S in ludicrous mode. Meanwhile, Nissan announced that its next-gen Leaf EV will be able to drive itself on the highway, and Toyota debuted a futuristic concept car that takes the wheel when drivers get sleepy. China is making a huge investment in high-speed rail to the tune of over $500 billion, and Israel is testing electric roads that wirelessly charge electric vehicles as they drive. Costa Rica has invested heavily in alternative energy, and it's paying off in spades: The nation 1.5 million electric vehicles a year. Israel is building the world's tallest solar tower, which will power 130,000 households once it's complete.

Jefferson Graham and his camera walks you through the busy, ultra-crowded booths in the CES Central Hall. The high-tech show is notorious for making it hard to get online and draining batteries. LAS VEGAS--CES teaches hard lessons about preserving a phone, tablet or laptop's battery life and connectivity, starting with this: If you value those things, don't go to CES. Having 175,000-plus people swarm around the city while constantly on e-mail, the Web, navigation apps and social networks will crumple wireless networks and leave batteries in the red. Getting through all this takes advance preparation--the kind that can also help you get through lesser battery and bandwidth challenges.

Stephen Hawking was celebrating his birthday early this year by taking in a movie. He turned 75 on Sunday, but the British theoretical physicist went to see "Rogue One: A Star Wars Story" in Cambridge, England, on Thursday. The pairing of the movie and the man was somewhat appropriate, what with the celebrated scientist viewing the latest installment of a celebrated science fiction film franchise. The man who was diagnosed more than a half-century ago with ALS, also known as Lou Gehrig's disease, has achieved much in his life, including beating the odds of survival with his medical condition. Typically given just 10 years to live from the time of diagnosis, Hawking has gone on to live in excess of five times that figure.