"The second edition was reviewed in JASA by Maiti (2004) … we now stand 10 years later with an even more impressive textbook that truly stands for what Bayesian data analysis should be. Quite a lot … this is truly the reference book for a graduate course on Bayesian statistics and not only Bayesian data analysis." Praise for the Second Edition: … it is simply the best all-around modern book focused on data analysis currently available. The second edition makes this an even more robust choice. I am thoroughly excited to have this book in hand to supplement course material and to offer research collaborators and clients at our consulting lab more sophisticated methods to solve their research problems.

Cancer is a tricky disease to treat. With more than one hundred known types, each responding differently to treatment depending on the person they're growing inside (and dozens of other factors), oncologists certainly have their work cut out for them. Machine learning could soon make their jobs a bit easier. IBM's supercomputer Watson has been applying machine learning to personalized cancer treatment for some time. After pouring over 600,000 medical reports and 1.5 million anonymized patient records and clinical trials, the data should help define the clearest path forward for doctors.

Digital art is sort of a vague term: GIFs? Art in which digital technology is central to the creative process. Some would say necessarily so. Computers--and the technologies we've built with them--have opened up huge new realms of possibility. Machine intelligence (think IBM's Watson or Google Brain) is at the forefront of these new discoveries.

Let's go through a high-level exploration of the evolution of computational hardware technologies with a focus on applications to machine learning (ML), and using cryptocurrency mining as an analogy. I posit that the machine learning industry is undergoing the same progression of hardware as cryptocurrency did years ago. Machine learning algorithms often consist of matrix (and tensor) operations. These calculations benefit greatly from parallel computing, which leads to model-training performed on graphics cards (rather than only on the CPU). Each step in this progression of technologies produces tremendous performance advantages.

Both questions found natural answers in the field of Recurrent Neural Networks (RNNs), which are brain-inspired general purpose computers that can learn parallel-sequential programs or algorithms encoded as weight matrices. Since then, a lot has happened. Some of the most successful applications in machine learning (including deep learning) are now driven by RNNs such as Long Short-Term Memory, e.g., speech recognition, video recognition, natural language processing, image captioning, time series prediction, etc. Reinforcement-learning and evolutionary RNNs are solving complex control tasks from raw video input. Many RNN-based methods learn sequential attention strategies. At this symposium, we will review the latest developments in all of these fields, and focus not only on RNNs, but also on learning machines in which RNNs interact with external memory such as neural Turing machines, memory networks, and related memory architectures such as fast weight networks and neural stack machines.

This is the conference, and here's my talk (will do Google hangout, just as with my recent talks in Bern, Strasbourg, etc): Through a series of examples, we consider problems with classical hypothesis testing, whether performed using classical p-values or confidence intervals, Bayes factors, or Bayesian inference using noninformative priors. We locate the problem not in the use of any particular statistical method but rather with larger problems of deterministic thinking and a misguided version of Popperianism in which the rejection of a straw-man null hypothesis is taken as confirmation of a preferred alternative. We suggest solutions involving multilevel modeling and informative Bayesian inference. The post Hypothesis Testing is a Bad Idea (my talk at Warwick, England, 2:30pm Thurs 15 Sept) appeared first on Statistical Modeling, Causal Inference, and Social Science. The post Hypothesis Testing is a Bad Idea (my talk at Warwick, England, 2:30pm Thurs 15 Sept) appeared first on All About Statistics.

Apple's iOS 10 update is available to download from September 13. As well as an ability to remove pre-installed stock apps, Apple has also introduced universal clipboard and'raise to wake', a feature that wakes an iPhone when it's taken out a pocket or lifted from a desk. Siri has also been updated to improve image searches, transcribe voicemails and write text messages. Apple is opening up the Siri to developers, too. Go to Settings, General and Check for updates.

The Chinese Internet giant Baidu Inc. has been makingbig progressinapplying deep learning neural networksto improve image recognition, language translation, search ranking and click prediction in advertising. Now, it's going to give a lot of it away. The company, often called "China's Google," will announce Thursday at the annual Baidu World conference that it's offering the artificial intelligence software that its own engineers have been using for years as open source onGitHub. It's code-namedPaddlePaddle, for PArallel Distributed Deep LEarning. Deep learningis the branch of machine learning that attempts to emulate the way neurons work in the human brain to find patterns in data representing sounds, images, and other data. Google, Facebook, Microsoft, IBM and other companies have also been making big breakthroughs thanks to the ability to pump massive amounts of data into these artificial neural networks.