Inverse Problems In High Dimensional Stochastic Systems Under Uncertainty.

Overview

Increasingly often, problems in modern medicine, quantitative finance, or social-networking involve tens of thousands of variables that interact with each other and jointly evolve over time. The states of these variables may correspond to the phenotype of a particular individual, the price of a security, or the current status of an individual's social networking profile. If these states are hidden to a researcher, additional information must be obtained to infer these hidden states based upon measurements of ...
See more details below
Other sellers (Paperback)
  • All (2) from $71.96   
  • New (2) from $71.96   
Sending request ...

More About This Book

Overview

Increasingly often, problems in modern medicine, quantitative finance, or social-networking involve tens of thousands of variables that interact with each other and jointly evolve over time. The states of these variables may correspond to the phenotype of a particular individual, the price of a security, or the current status of an individual's social networking profile. If these states are hidden to a researcher, additional information must be obtained to infer these hidden states based upon measurements of other variables, knowledge of the interacting network structure, and any dynamics that model the evolution of these states. This dissertation is an attempt to address general problems regarding reasoning under uncertainty in such spatio-temporal models but with an emphasis to applications in predictive health and disease in a loosely monitored population of individuals. The motivation is highly interdisciplinary and draws on tools and concepts from machine learning, statistics, epidemiology, bioinformatics, and physics.

We begin by presenting a solution to recursively sampling the best subset of nodes/variables that elicit the largest expected information gain of all sampled and un-sampled nodes in a large spatio-temporal complex network. We use methods from information theory and approximate Bayesian filtering to achieve this task. We then present a tractable method for empirically estimating the spatio-temporal graphical model structure corresponding to the "susceptible", "infected", and "recovered" (SIR) model of mathematical epidemiology. Here, we formulate the problem as an ℓ1-penalized likelihood convex program and produce network detection performance superior to other comparable state of the art methods. We present a logistic regression classifier that is robust to worst-case bounded measurement uncertainty. The proposed method produces superior worst-case detection performance to the standard ℓ 1-logistic regression classifier on a Human rhinovirus (HRV) gene expression data set. The relationship between sparsity promoting regularization penalties and robustness to bounded measurement uncertainty is also established. The final chapter concludes with identifying the appropriate basis functions used in a classification model when the data is both high-dimensional and temporally sampled with ultimate goal of discriminating between multiple states/labels, e.g., phenotypes. We utilize Gaussian Processes and ℓ1-logistic regression to accomplish this task and apply it to a human gene expression time-series data set resulting from a challenge study inoculation with Human Influenza A/H3N2, HRV, and Human respiratory syncytial virus (RSV).

Read More Show Less

Product Details

  • ISBN-13: 9781244770072
  • Publisher: BiblioLabsII
  • Publication date: 10/1/2011
  • Pages: 126
  • Product dimensions: 7.44 (w) x 9.69 (h) x 0.27 (d)

Customer Reviews

Be the first to write a review
( 0 )
Rating Distribution

5 Star

(0)

4 Star

(0)

3 Star

(0)

2 Star

(0)

1 Star

(0)

Your Rating:

Your Name: Create a Pen Name or

Barnes & Noble.com Review Rules

Our reader reviews allow you to share your comments on titles you liked, or didn't, with others. By submitting an online review, you are representing to Barnes & Noble.com that all information contained in your review is original and accurate in all respects, and that the submission of such content by you and the posting of such content by Barnes & Noble.com does not and will not violate the rights of any third party. Please follow the rules below to help ensure that your review can be posted.

Reviews by Our Customers Under the Age of 13

We highly value and respect everyone's opinion concerning the titles we offer. However, we cannot allow persons under the age of 13 to have accounts at BN.com or to post customer reviews. Please see our Terms of Use for more details.

What to exclude from your review:

Please do not write about reviews, commentary, or information posted on the product page. If you see any errors in the information on the product page, please send us an email.

Reviews should not contain any of the following:

  • - HTML tags, profanity, obscenities, vulgarities, or comments that defame anyone
  • - Time-sensitive information such as tour dates, signings, lectures, etc.
  • - Single-word reviews. Other people will read your review to discover why you liked or didn't like the title. Be descriptive.
  • - Comments focusing on the author or that may ruin the ending for others
  • - Phone numbers, addresses, URLs
  • - Pricing and availability information or alternative ordering information
  • - Advertisements or commercial solicitation

Reminder:

  • - By submitting a review, you grant to Barnes & Noble.com and its sublicensees the royalty-free, perpetual, irrevocable right and license to use the review in accordance with the Barnes & Noble.com Terms of Use.
  • - Barnes & Noble.com reserves the right not to post any review -- particularly those that do not follow the terms and conditions of these Rules. Barnes & Noble.com also reserves the right to remove any review at any time without notice.
  • - See Terms of Use for other conditions and disclaimers.
Search for Products You'd Like to Recommend

Recommend other products that relate to your review. Just search for them below and share!

Create a Pen Name

Your Pen Name is your unique identity on BN.com. It will appear on the reviews you write and other website activities. Your Pen Name cannot be edited, changed or deleted once submitted.

 
Your Pen Name can be any combination of alphanumeric characters (plus - and _), and must be at least two characters long.

Continue Anonymously

    If you find inappropriate content, please report it to Barnes & Noble
    Why is this product inappropriate?
    Comments (optional)