117,346 research outputs found
Gaussian surrogate models for Bayesian inverse problems
No full textSolving Bayesian inverse problems using Markov Chain Monte Carlo (MCMC) methods poses significant computational challenges due to the extensive numerical simulations required for each sample. To address this issue, surrogate models are often employed to approximate the complex models, thereby reducing computational costs.
This thesis focuses on the use of Gaussian surrogate models for Bayesian inverse problems associated with linear partial differential equations, particularly in scenarios when only limited training data are available. To enhance the accuracy and robustness of prediction without requiring additional observational data, we investigate the physics-informed Gaussian process regression (PI-GPR) method which provides a flexible framework for integrating physical information into the Gaussian process, and extend the method to construct different approximate posteriors for solving the Bayesian inverse problems. Benefiting from the nature of Gaussian process regression as a statistical model, the error of approximation can be quantified and integrated into the approximation of posteriors. Meanwhile, the gradient of the approximate posteriors based on Gaussian surrogate models can be analytically computed, enabling the use of gradient-based MCMC samplers like the Metropolis-adjusted Langevin algorithm (MALA) for efficient sampling. Finally, the approximate posterior can be used in the delayed-acceptance Metropolis-Hastings sampling algorithm, which helps reject unlikely candidates with a much lower cost and hence significantly reduces the overall computational cost
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Square Dancing with the Stars to Enhance Dynamic Hirschman Linkages?
No full textIn this Presidential Address, the author takes the reader on a reconnaissance of his life and time as a regional scientist. He points out scenery he found scintillating along the way, hoping that some may pick up the banner and chew on a few of the ideas for a while. He suggests a revisit to Albert O. Hirschman’s notion of key sectors and more empirical analysis related to Marcus Berliant’s and Masahisa Fujita’s notion of knowledge creation and transfer.Presidential Address, San Antonio, Texas, March 29, 2014 (53rd Meetings of the Southern Regional Science Association
Appropriate Similarity Measures for Author Cocitation Analysis
No full textWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
The theory and application of non-stationary and deep Gaussian processes in regression problems
No full textThe focus of this work is the convergence of non-stationary and deep Gaussian process
regression. More precisely, we follow a Bayesian approach to regression or interpolation,
where the prior placed on the unknown function f is a non-stationary or deep Gaussian
process, and we derive convergence rates of the posterior mean to the true function f in
terms of the number of observed training points. In some cases, we also show convergence
of the posterior variance to zero. The only assumption imposed on the function f is that
it is an element of a certain reproducing kernel Hilbert space, which we in particular cases
show to be norm-equivalent to a Sobolev space. Our analysis includes the case of estimated
hyper-parameters in the covariance kernels employed, both in an empirical Bayes setting and
the particular hierarchical setting constructed through deep Gaussian processes. We consider
the settings of noise-free or noisy observations on deterministic or random training points.
We establish general assumptions sufficient for the convergence of deep Gaussian process
regression, along with explicit examples demonstrating the fulfilment of these assumptions.
Specifically, our examples require that the Hölder or Sobolev norms of the penultimate layer
are bounded almost surely.
In addition to these theoretical results, we present numerical simulations that further validate
our findings. For instance, we demonstrate that even with a ’bad choice’ of a non-stationary
kernel- where the prior does not match the non-stationary structure of the unknown function
f- the theoretical convergence results remain robust. Moreover, the simulations reveal
that certain theoretical assumptions for deep Gaussian processes, such as the need to
reduce regularity hyperparameters in ascending layers of the deep Gaussian process, are
often unnecessary in practice. We apply non-stationary and deep Gaussian processes to a
variety of synthetic and real-world datasets, both to illustrate the theoretical insights and
to benchmark the performance of our methods
Letter from unknown writer to Jesse L. Boyce
No full textLetter to Jesse L. Boyce from unknown author (possibly Jack) about the investigation into the powder magazine located in the Grand Canyon. Some personal news is included in the letter such as the writer's marriage to the daughter of C.A. Taylor, former Supervisor of Cochise County
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Sarah L. Blum Author Visit - Warrior Nurse: PTSD and Healing
No full textHear Sarah L. Blum, author of Women Under Fire: Abuse in the Military, discuss her newest book, Warrior Nurse: PTSD and Healing followed by a Q&A and book signing.
Sarah L. Blum is a decorated Vietnam veteran who served as an operating room nurse during the intense fighting of 1967. In recognition of her service, she was awarded the Army Commendation Medal.
Sponsored by CWU Veterans Center and CWU Libraries.https://digitalcommons.cwu.edu/libraryevents/1252/thumbnail.jp
Lillian L. Lambert, Author, Speaker, and Entrepreneur
No full textLillian L. Lambert, Author, Speaker, and Entrepreneu
Letter to Alfred L. Shoemaker, February 10, 1948
No full textA handwritten letter from an unknown author addressed to Alfred L. Shoemaker, dated February 10, 1948. Within, the author discusses the Pennsylvania Dutch word for Ash Wednesday, along with traditions associated with this day.https://digitalcommons.ursinus.edu/shoemaker_documents/1118/thumbnail.jp
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