We describe a Bayesian approach for learning Bayesian networks from a combination of prior knowledge and statistical data. First and foremost, we develop a methodology for assessing informative priors needed for learning. Our approach is derived from a set of assumptions made previously as well as the assumption oflikelihood equivalence, which says that data should not help to discriminate network structures that represent the same assertions of conditional independence. We show that likelihood equivalence when combined with previously made assumptions implies that the user's priors for network parameters can be encoded in a single Bayesian network for the next case to be seen--aprior network--and a single measure of confidence for that network. Second, using these priors, we show how to compute the relative posterior probabilities of network structures given data.

In this paper, we bring techniques from operations research to bear on the problem of choosing optimal actions in partially observable stochastic domains. We begin by introducing the theory of Markov decision processes (mdps) and partially observable MDPs (pomdps). We then outline a novel algorithm for solving pomdps off line and show how, in some cases, a finite-memory controller can be extracted from the solution to a POMDP. We conclude with a discussion of how our approach relates to previous work, the complexity of finding exact solutions to pomdps, and of some possibilities for finding approximate solutions.

Several of the preprocessing steps necessary for indexing as discussed in Chapter 2: detecting a document's encoding (ASCII, Unicode UTF-8 etc; page 2.1.1);

In rare disease physician targeting, a major challenge is how to identify physicians who are treating diagnosed or underdiagnosed rare diseases patients. Rare diseases have extremely low incidence rate. For a specified rare disease, only a small number of patients are affected and a fractional of physicians are involved. The existing targeting methodologies, such as segmentation and profiling, are developed under mass market assumption. They are not suitable for rare disease market where the target classes are extremely imbalanced. The authors propose a graphical model approach to predict targets by jointly modeling physician and patient features from different data spaces and utilizing the extra relational information. Through an empirical example with medical claim and prescription data, the proposed approach demonstrates better accuracy in finding target physicians. The graph representation also provides visual interpretability of relationship among physicians and patients. The model can be extended to incorporate more complex dependency structures. This article contributes to the literature of exploring the benefit of utilizing relational dependencies among entities in healthcare industry.

We introduce a new dynamical system for sequentially observed multivariate count data. This model is based on the gamma--Poisson construction---a natural choice for count data---and relies on a novel Bayesian nonparametric prior that ties and shrinks the model parameters, thus avoiding overfitting. We present an efficient MCMC inference algorithm that advances recent work on augmentation schemes for inference in negative binomial models. Finally, we demonstrate the model's inductive bias using a variety of real-world data sets, showing that it exhibits superior predictive performance over other models and infers highly interpretable latent structure.

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The problem with using the human voice to control computers is well known and well documented: it doesn't always work. You can find yourself adopting the aggressive tone of a belligerent tourist in a foreign land while digital assistants employ a range of apologetic responses ("I'm sorry, I didn't quite get that", "I'm sorry, I didn't understand the question"). We throw our arms up and complain about their shortcomings. Plenty of us have tried them, plenty of us have dismissed them as a waste of time. We tend not to hear about them doing the job perfectly well, because few people write impassioned tweets or blog posts about things that work flawlessly.

The Teachable Agents Project combines research from computer science, psychology, and education to develop computer-based learning environments. These environments utilize animated pedagogical agents to facilitate science learning and the development of self-regulated learning skills. The use of animated agents allows us to extend the cognitive scaffolding provided by various computer tools and representations (e.g., searchable text, simulations, concept maps, etc.) by embedding them in productive and motivating social-constructive interactions (e.g., peer teaching, collaboration, and assessment). Current projects include Betty's Brain, a learning-by-teaching environment for science learning; CTSiM, an environment for understanding science through a computational thinking framework; SimSelf, a relatively new project that focuses on teaching students about self-regulation and metacognition in the context of science learning; and C3STEM, a community-situated, challenge-based, collaborative STEM learning environment. Our learning environments also include extensive logging of students' interactions with the system and agents.

While machine learning originated as a subfield of artificial intelligence--the area of computer science dedicated to creating humanlike intelligence in computers--it's expanded beyond the boundaries of A.I. into data science and expert systems. But machine learning is fundamentally different from much of what we think of as programming. When we think of a computer program (or the algorithm a program implements), we generally think of a human engineer giving a set of instructions to a computer, telling it how to handle certain inputs that will generate certain outputs. The state maintained by the program changes over time--a Web browser keeps track of which pages it's displaying and responds to user input by (ideally) reacting in a determinate and predictable fashion--but the logic of the program is essentially described by the code written by the human. Machine learning, in many of its forms, is about building programs that themselves build programs.

A system being developed by Christopher Amato, a postdoc at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), can perform security-camera analysis to identify potential terrorists or illegal entry more accurately and in a fraction of the time it would take a human camera operator. "You can't have a person staring at every single screen, and even if you did the person might not know exactly what to look for," Amato says. "For example, a person is not going to be very good at searching through pages and pages of faces to try to match [an intruder] with a known criminal or terrorist." Existing computer vision systems designed to carry out this task automatically tend to be fairly slow, Amato says. "Sometimes it's important to come up with an alarm immediately, even if you are not yet positive exactly what it is happening," he says.