Engineering topics

Hanson, Robin

Publications and source records attributed to Hanson, Robin.

5 records

NASA NTRS1996

Market-Based Approaches to Managing Science Return from Planetary Missions

A research plan is described for the design and testing of a method for the planning and negotiation of science observations. The research plan is presented in relation to the fact that the current method, which involves a hierarchical process of science working groups, is unsuitable for the planning of the Cassini mission. The research plan involves the market-based approach in which participants are allocated budgets of scheduling points. The points are used to provide an intensity of preference for the observations being scheduled. In this way, the schedulers do not have to limit themselves to solving major conflicts, but try to maximize the number of scheduling points that result in a conflict-free timeline. Incentives are provided for the participants by the fixed budget concerning their tradeoff decisions. A degree of feedback is provided in the process so that the schedulers may rebid based on the current timeline.

Wessen, Randii R.↗

NASA NTRS1994

AutoClass: A Bayesian Approach to Classification

We describe a Bayesian approach to the untutored discovery of classes in a set of cases, sometimes called finite mixture separation or clustering. The main difference between clustering and our approach is that we search for the "best" set of class descriptions rather than grouping the cases themselves. We describe our classes in terms of a probability distribution or density function, and the locally maximal posterior probability valued function parameters. We rate our classifications with an approximate joint probability of the data and functional form, marginalizing over the parameters. Approximation is necessitated by the computational complexity of the joint probability. Thus, we marginalize w.r.t. local maxima in the parameter space. We discuss the rationale behind our approach to classification. We give the mathematical development for the basic mixture model and describe the approximations needed for computational tractability. We instantiate the basic model with the discrete Dirichlet distribution and multivariant Gaussian density likelihoods. Then we show some results for both constructed and actual data.

Stutz, John↗

NASA NTRS1992

Bayesian Classification Scheme

Scheme derived via statistical approach identifies classes in sets of data. Determines probable number of classes, probabilistic descriptions of classes, and probability that each object is member of each class. Scheme applicable to real-valued, discrete or continuous data presented in form of parameter vectors representing attributes of objects.

Stutz, John↗

NASA NTRS1991

Autoclass: An automatic classification system

The task of inferring a set of classes and class descriptions most likely to explain a given data set can be placed on a firm theoretical foundation using Bayesian statistics. Within this framework, and using various mathematical and algorithmic approximations, the AutoClass System searches for the most probable classifications, automatically choosing the number of classes and complexity of class descriptions. A simpler version of AutoClass has been applied to many large real data sets, has discovered new independently-verified phenomena, and has been released as a robust software package. Recent extensions allow attributes to be selectively correlated within particular classes, and allow classes to inherit, or share, model parameters through a class hierarchy. The mathematical foundations of AutoClass are summarized.

Stutz, John↗

NASA NTRS1991

Bayesian classification theory

The task of inferring a set of classes and class descriptions most likely to explain a given data set can be placed on a firm theoretical foundation using Bayesian statistics. Within this framework and using various mathematical and algorithmic approximations, the AutoClass system searches for the most probable classifications, automatically choosing the number of classes and complexity of class descriptions. A simpler version of AutoClass has been applied to many large real data sets, has discovered new independently-verified phenomena, and has been released as a robust software package. Recent extensions allow attributes to be selectively correlated within particular classes, and allow classes to inherit or share model parameters though a class hierarchy. We summarize the mathematical foundations of AutoClass.

Hanson, Robin↗