Mean Field Theory for Sigmoid Belief Networks

We develop a mean field theory for sigmoid belief networks based on ideas from statistical mechanics. Our mean field theory provides a tractable approximation to the true probability distribution in these networks; it also yields a lower bound on the likelihood of evidence. We demonstrate the utility of this framework on a benchmark problem in statistical pattern recognition---the classification of handwritten digits.Comment: See http://www.jair.org/ for any accompanying file

A timer inventory based upon manual and automated analysis of ERTS-1 and supporting aircraft data using multistage probability sampling

A quasi-operational study demonstrating that a timber inventory based on manual and automated analysis of ERTS-1, supporting aircraft data and ground data was made using multistage sampling techniques. The inventory proved to be a timely, cost effective alternative to conventional timber inventory techniques. The timber volume on the Quincy Ranger District of the Plumas National Forest was estimated to be 2.44 billion board feet with a sampling error of 8.2 percent. Costs per acre for the inventory procedure at 1.1 cent/acre compared favorably with the costs of a conventional inventory at 25 cents/acre. A point-by-point comparison of CALSCAN-classified ERTS data with human-interpreted low altitude photo plots indicated no significant differences in the overall classification accuracies

Stochastic Variational Inference

We develop stochastic variational inference, a scalable algorithm for approximating posterior distributions. We develop this technique for a large class of probabilistic models and we demonstrate it with two probabilistic topic models, latent Dirichlet allocation and the hierarchical Dirichlet process topic model. Using stochastic variational inference, we analyze several large collections of documents: 300K articles from Nature, 1.8M articles from The New York Times, and 3.8M articles from Wikipedia. Stochastic inference can easily handle data sets of this size and outperforms traditional variational inference, which can only handle a smaller subset. (We also show that the Bayesian nonparametric topic model outperforms its parametric counterpart.) Stochastic variational inference lets us apply complex Bayesian models to massive data sets

Assessment of public health impact of work-related asthma

<p>Abstract</p> <p>Background</p> <p>Asthma is among the most common chronic diseases in working-aged populations and occupational exposures are important causal agents. Our aims were to evaluate the best methods to assess occurrence, public health impact, and burden to society related to occupational or work-related asthma and to achieve comparable estimates for different populations.</p> <p>Methods</p> <p>We addressed three central questions: <b>1: What is the best method to assess the occurrence of occupational asthma? </b>We evaluated: 1) assessment of the occurrence of occupational asthma <it>per se</it>, and 2) assessment of adult-onset asthma and the population attributable fractions due to specific occupational exposures. <b>2: What are the best methods to assess public health impact and burden to society related to occupational or work-related asthma? </b>We evaluated methods based on assessment of excess burden of disease due to specific occupational exposures. <b>3: How to achieve comparable estimates for different populations? </b>We evaluated comparability of estimates of occurrence and burden attributable to occupational asthma based on different methods.</p> <p>Results</p> <p>Assessment of the occurrence of occupational asthma <it>per se </it>can be used in countries with good coverage of the identification system for occupational asthma, i.e. countries with well-functioning occupational health services. Assessment based on adult-onset asthma and population attributable fractions due to specific occupational exposures is a good approach to estimate the occurrence of occupational asthma at the population level. For assessment of public health impact from work-related asthma we recommend assessing excess burden of disease due to specific occupational exposures, including excess incidence of asthma complemented by an assessment of disability from it. International comparability of estimates can be best achieved by methods based on population attributable fractions.</p> <p>Conclusions</p> <p>Public health impact assessment for occupational asthma is central in prevention and health policy planning and could be improved by purposeful development of methods for assessing health benefits from preventive actions. Registry-based methods are suitable for evaluating time-trends of occurrence at a given population but for international comparisons they face serious limitations. Assessment of excess burden of disease due to specific occupational exposure is a useful measure, when there is valid information on population exposure and attributable fractions.</p

Trapping atoms on a transparent permanent-magnet atom chip

We describe experiments on trapping of atoms in microscopic magneto-optical traps on an optically transparent permanent-magnet atom chip. The chip is made of magnetically hard ferrite-garnet material deposited on a dielectric substrate. The confining magnetic fields are produced by miniature magnetized patterns recorded in the film by magneto-optical techniques. We trap Rb atoms on these structures by applying three crossed pairs of counter-propagating laser beams in the conventional magneto-optical trapping (MOT) geometry. We demonstrate the flexibility of the concept in creation and in-situ modification of the trapping geometries through several experiments.Comment: Modifications: A) Reference I. Barb et al., Eur. Phys. JD, 35, 75 (2005) added. B)Sentence rewritten: We routinely capture more than 10^6 atoms in a micro-MOT on a magnetized pattern. C) The magnetic field strengths are now given in Teslas. D) The second sentence in the fourth paragraph has been rewritten in order to more clearly describe the geometry and purpose of the compensation coils.E) In the seventh paragraph we have rewritten the sentence about the creation of the external magnetic field for the magnetic-domain patterning. F) In the ninth paragraph, we clarify the way to shift the trap center. G) Caption of Fig. 4 changed. H) We have modified paragraph 12 to improve the description on the guiding of the trap center along a toroidal pattern. I) The last two sentences of the manuscript have been rewritte

Variational Probabilistic Inference and the QMR-DT Network

We describe a variational approximation method for efficient inference in large-scale probabilistic models. Variational methods are deterministic procedures that provide approximations to marginal and conditional probabilities of interest. They provide alternatives to approximate inference methods based on stochastic sampling or search. We describe a variational approach to the problem of diagnostic inference in the `Quick Medical Reference' (QMR) network. The QMR network is a large-scale probabilistic graphical model built on statistical and expert knowledge. Exact probabilistic inference is infeasible in this model for all but a small set of cases. We evaluate our variational inference algorithm on a large set of diagnostic test cases, comparing the algorithm to a state-of-the-art stochastic sampling method

Respiratory Infections Precede Adult-Onset Asthma

BACKGROUND: Respiratory infections in early life are associated with an increased risk of developing asthma but there is little evidence on the role of infections for onset of asthma in adults. The objective of this study was to assess the relation of the occurrence of respiratory infections in the past 12 months to adult-onset asthma in a population-based incident case-control study of adults 21-63 years of age. METHODS/PRINCIPAL FINDINGS: We recruited all new clinically diagnosed cases of asthma (n = 521) during a 2.5-year study period and randomly selected controls (n = 932) in a geographically defined area in South Finland. Information on respiratory infections was collected by a self-administered questionnaire. The diagnosis of asthma was based on symptoms and reversible airflow obstruction in lung function measurements. The risk of asthma onset was strongly increased in subjects who had experienced in the preceding 12 months lower respiratory tract infections (including acute bronchitis and pneumonia) with an adjusted odds ratio (OR) 7.18 (95% confidence interval [CI] 5.16-9.99), or upper respiratory tract infections (including common cold, sinusitis, tonsillitis, and otitis media) with an adjusted OR 2.26 (95% CI 1.72-2.97). Individuals with personal atopy and/or parental atopy were more susceptible to the effects of respiratory infections on asthma onset than non-atopic persons. CONCLUSIONS/SIGNIFICANCE: This study provides new evidence that recently experienced respiratory infections are a strong determinant for adult-onset asthma. Reducing such infections might prevent onset of asthma in adulthood, especially in individuals with atopy or hereditary propensity to it

Generalized Permutohedra from Probabilistic Graphical Models

A graphical model encodes conditional independence relations via the Markov properties. For an undirected graph these conditional independence relations can be represented by a simple polytope known as the graph associahedron, which can be constructed as a Minkowski sum of standard simplices. There is an analogous polytope for conditional independence relations coming from a regular Gaussian model, and it can be defined using multiinformation or relative entropy. For directed acyclic graphical models and also for mixed graphical models containing undirected, directed and bidirected edges, we give a construction of this polytope, up to equivalence of normal fans, as a Minkowski sum of matroid polytopes. Finally, we apply this geometric insight to construct a new ordering-based search algorithm for causal inference via directed acyclic graphical models.Comment: Appendix B is expanded. Final version to appear in SIAM J. Discrete Mat