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Results for “PROBABILITY DISTRIBUTION”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1979

Higher-order skewness and excess coefficients of some probability distributions applicable to optical propagation phenomena

Expressions are derived for higher-order skewness and excess coefficients using central moments and cumulants up to 8th order. These coefficients are then calculated for three probability distributions: (1) Log-normal, (2) Rice-Nakagami, and (3) Gamma distributions. Curves are given to shown the variation of skewness with excess coefficients for these distributions. These curves are independent of the particular distribution parameters. This method is useful for studying fluctuating phenomena, which obey non-Gaussian statistics.

Majumdar, A. K.↗
NASA NTRS1992

Caustic-induced features in microlensing magnification probability distributions

Numerical simulations have uncovered a previously unrecognized 'bump' in the macroimage magnification probabilities produced by a planar distribution of point masses. The result could be relevant to cases of microlensing by star fields in single galaxies, for which this lensing geometry is an excellent approximation. The bump is produced by bright pairs of microimages formed by sources lying near the caustics of the lens. The numerically calculated probabilities for the magnifications in the range between 3 and 30 are significantly higher than those given by the asymptotic relation derived by Schneider. The bump present in the two-dimensional lenses appears not to exist in the magnification probability distribution produced by a fully three-dimensional lens.

Rauch, Kevin P.↗
DOE OSTI2025

The Forward Master Equation for the Joint Neutron-Photon Number Probability Distribution

The model is similar to the Binary Fission Model (BFM) in chapter three of the Stochastic Neutronics Primer Volume I, however, we add photons as a product of induced fission events (IFEs). We will find that tracking the population of an additional particle adds an additional layer of complexity because we are now looking for a joint probability distribution.

42 ENGINEERING↗
NASA NTRSDate not supplied

Hopping with an Adaptive Hop Probability Distribution

Monotonic Basin Hopping (MBH) is a stochastic global search technique that may be used to design complex interplanetary trajectories. Previous research on MBH empirically demonstrated that a bi-polar Pareto distribution is an effective way to generate search “hops.” However, no analytical foundation exists to explain this performance. In this work we provide the beginning of that analytical foundation and also introduce a new variant of MBH that uses an adaptive probability distribution to generate the hops. The technique is demonstrated on historical interplanetary trajectory design problems.

A C Englander↗
NASA NTRS1973

Bistatic-radar estimation of surface-slope probability distributions with applications to the moon.

A method for extracting surface-slope frequency distributions from bistatic-radar data has been developed and applied to the lunar surface. Telemetry transmissions from orbiting Apollo spacecraft were received on the earth after reflection from the lunar surface. The echo-frequency spectrum was related analytically to the probability distribution of lunar slopes. Standard regression techniques were used to solve the inverse problem of finding slope distributions from observed echo-frequency spectra. Data taken simultaneously at two wavelengths, 13 and 116 cm, have yielded diverse slope statistics.

Parker, M. N.↗
NASA NTRS2004

Use of Probability Distribution Functions for Discriminating Between Cloud and Aerosol in Lidar Backscatter Data

In this paper we describe the algorithm hat will be used during the upcoming Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission for discriminating between clouds and aerosols detected in two wavelength backscatter lidar profiles. We first analyze single-test and multiple-test classification approaches based on one-dimensional and multiple-dimensional probability density functions (PDFs) in the context of a two-class feature identification scheme. From these studies we derive an operational algorithm based on a set of 3-dimensional probability distribution functions characteristic of clouds and aerosols. A dataset acquired by the Cloud Physics Lidar (CPL) is used to test the algorithm. Comparisons are conducted between the CALIPSO algorithm results and the CPL data product. The results obtained show generally good agreement between the two methods. However, of a total of 228,264 layers analyzed, approximately 5.7% are classified as different types by the CALIPSO and CPL algorithm. This disparity is shown to be due largely to the misclassification of clouds as aerosols by the CPL algorithm. The use of 3-dimensional PDFs in the CALIPSO algorithm is found to significantly reduce this type of error. Dust presents a special case. Because the intrinsic scattering properties of dust layers can be very similar to those of clouds, additional algorithm testing was performed using an optically dense layer of Saharan dust measured during the Lidar In-space Technology Experiment (LITE). In general, the method is shown to distinguish reliably between dust layers and clouds. The relatively few erroneous classifications occurred most often in the LITE data, in those regions of the Saharan dust layer where the optical thickness was the highest.

Liu, Zhaoyan↗
NASA NTRS1993

Development and application of an empirical probability distribution for the prediction error of re-entry body maximum dynamic pressure

The relationship between actual and predicted re-entry maximum dynamic pressure is characterized using a probability density function and a cumulative distribution function derived from sounding rocket flight data. This paper explores the properties of this distribution and demonstrates applications of this data with observed sounding rocket re-entry body damage characteristics to assess probabilities of sustaining various levels of heating damage. The results from this paper effectively bridge the gap existing in sounding rocket reentry analysis between the known damage level/flight environment relationships and the predicted flight environment.

Lanzi, R. James↗
NASA NTRS1980

An experimental study of the surface elevation probability distribution and statistics of wind-generated waves

Laboratory experiments were performed to measure the surface elevation probability density function and associated statistical properties for a wind-generated wave field. The laboratory data along with some limited field data were compared. The statistical properties of the surface elevation were processed for comparison with the results derived from the Longuet-Higgins (1963) theory. It is found that, even for the highly non-Gaussian cases, the distribution function proposed by Longuet-Higgins still gives good approximations.

Huang, N. E.↗
NASA NTRS1988

Supervised learning of probability distributions by neural networks

Supervised learning algorithms for feedforward neural networks are investigated analytically. The back-propagation algorithm described by Werbos (1974), Parker (1985), and Rumelhart et al. (1986) is generalized by redefining the values of the input and output neurons as probabilities. The synaptic weights are then varied to follow gradients in the logarithm of likelihood rather than in the error. This modification is shown to provide a more rigorous theoretical basis for the algorithm and to permit more accurate predictions. A typical application involving a medical-diagnosis expert system is discussed.

Baum, Eric B.↗
NASA NTRS1991

Retrieval of monthly rainfall indices from microwave radiometric measurements using probability distribution functions

An algorithm for the estimation of monthly rain totals for 5 deg cells over the ocean from histograms of SSM/I brightness temperatures has been developed. There are three novel features to this algorithm. First, it uses knowledge of the form of the rainfall intensity probability density function to augment the measurements. Second, a linear combination of the 19.35 and 22.235 GHz channels has been employed to reduce the impact of variability of water vapor. Third, an objective technique has been developed to estimate the rain layer thickness from the 19.35- and 22.235-GHz brightness temperature histograms. Comparison with climatologies and the GATE radar observations suggest that the estimates are reasonable in spite of not having a beam-filling correction. By-products of the retrievals indicate that the SSM/I instrument noise level and calibration stability are quite good.

Wilheit, Thomas T.↗
NASA NTRS2002

Visualizing 2D Probability Distributions from Satellite Image-Derived Data

Creating maps of biophysical and geophysical variables using Earth Observing System (EOS) satellite image data is an important component of Earth science. These 2D maps have a single value at every location and standard techniques are used to visualize them. Current tools fall short, however, when it is necessary to describe a distribution of values at each location. Distributions may represent a frequency of occurrence over time, frequency of occurrence from multiple runs of an ensemble forecast or possible values from an uncertainty model. 'Distribution data sets' are described, then a case study is presented to visualize such 2D distributions. Distribution data sets are different from multivariate data sets in the sense that the values are for a single variable instead of multiple variables. Our case study data consists of multiple realizations of percent forest cover, generated using a geostatistical technique that combines ground measurements and satellite imagery to model uncertainty about forest cover. We present several approaches for analyzing and visualizing such data sets. The first is a pixel-wise analysis of the probability density functions for the 2D image while the second is an analysis of features identified within the image. Such pixel-wise and feature-wise views will give Earth scientists a more complete understanding of distribution data sets.

Kao, David↗
DOE OSTI2025

Using probability distribution function as a scaling approach to incorporate soil heterogeneity into biogeochemical models for greenhouse gas predictions (Final Technical Report)

The project investigated biogeochemical processes at terrestrial-aquatic interfaces (TAIs), focusing on soil microsite heterogeneity and its impact on greenhouse gas (GHG) fluxes. Using laboratory experiments, modeling, and data integration, researchers explored redox-driven microbial processes under fluctuating hydrological conditions. Key advancements included modifying the DAMM-GHG model to incorporateelectron acceptor availability and enhancing the AquaMEND model for improved microbial metabolism representation. Results highlighted microsite redox variability as a key driver of GHG fluxes, informing Earth system models. The project fostered interdisciplinary collaborations, student training, and the development of novel modeling frameworks to improve Earth'senergy budget.

54 ENVIRONMENTAL SCIENCES↗