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Empirical Analysis of Predictive Algorithms for Collaborative Filtering
Several algorithms designed for collaborative filtering or recommender systems are described, including techniques based on correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods, to compare the predictive accuracy of the various methods in a set of representative problem domains.
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data
A methodology for assessing informative priors needed for learning Bayesian networks from a combination of prior knowledge and statistical data is developed and how to compute the relative posterior probabilities of network structures given data is shown.
A Hexanucleotide Repeat Expansion in C9ORF72 Is the Cause of Chromosome 9p21-Linked ALS-FTD
Efficient Control of Population Structure in Model Organism Association Mapping
A new method, efficient mixed-model association (EMMA), which corrects for population structure and genetic relatedness in model organism association mapping and takes advantage of the specific nature of the optimization problem in applying mixed models for association mapping, which allows for substantially increase the computational speed and reliability of the results.
A Tutorial on Learning with Bayesian Networks
- D. Heckerman
- Computer ScienceInnovations in Bayesian Networks
- 1 February 1999
Methods for constructing Bayesian networks from prior knowledge are discussed and methods for using data to improve these models are summarized, including techniques for learning with incomplete data.
A Bayesian Approach to Filtering Junk E-Mail
This work examines methods for the automated construction of filters to eliminate such unwanted messages from a user’s mail stream, and shows the efficacy of such filters in a real world usage scenario, arguing that this technology is mature enough for deployment.
FaST linear mixed models for genome-wide association studies
- C. Lippert, J. Listgarten, Y. Liu, C. Kadie, R. Davidson, D. Heckerman
- Computer ScienceNature Methods
- 1 October 2011
We describe factored spectrally transformed linear mixed models (FaST-LMM), an algorithm for genome-wide association studies (GWAS) that scales linearly with cohort size in both run time and memory…
Dependency Networks for Inference, Collaborative Filtering, and Data Visualization
- D. Heckerman, D. M. Chickering, Christopher Meek, Robert Rounthwaite, C. Kadie
- Computer ScienceJ. Mach. Learn. Res.
- 1 September 2001
This work describes a graphical model for probabilistic relationships--an alternative to the Bayesian network--called a dependency network and identifies several basic properties of this representation and describes a computationally efficient procedure for learning the graph and probability components from data.
The Lumière Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users
This work reviews work on Bayesian user models that can be employed to infer a user's needs by considering a users' background, actions, and queries and proposes an overall architecture for an intelligent user interface.