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Results for “Probability estimates”

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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS1976

Estimation of the probability of error without ground truth and known a priori probabilities

The probability of error or, alternatively, the probability of correct classification (PCC) is an important criterion in analyzing the performance of a classifier. Labeled samples (those with ground truth) are usually employed to evaluate the performance of a classifier. Occasionally, the numbers of labeled samples are inadequate, or no labeled samples are available to evaluate a classifier's performance; for example, when crop signatures from one area from which ground truth is available are used to classify another area from which no ground truth is available. This paper reports the results of an experiment to estimate the probability of error using unlabeled test samples (i.e., without the aid of ground truth).

Havens, K. A.↗
NASA NTRS1990

Estimating the exceedance probability of rain rate by logistic regression

Recent studies have shown that the fraction of an area with rain intensity above a fixed threshold is highly correlated with the area-averaged rain rate. To estimate the fractional rainy area, a logistic regression model, which estimates the conditional probability that rain rate over an area exceeds a fixed threshold given the values of related covariates, is developed. The problem of dependency in the data in the estimation procedure is bypassed by the method of partial likelihood. Analyses of simulated scanning multichannel microwave radiometer and observed electrically scanning microwave radiometer data during the Global Atlantic Tropical Experiment period show that the use of logistic regression in pixel classification is superior to multiple regression in predicting whether rain rate at each pixel exceeds a given threshold, even in the presence of noisy data. The potential of the logistic regression technique in satellite rain rate estimation is discussed.

Chiu, Long S.↗
NASA NTRS1985

Estimate of the probability of a lightning strike to the Galileo probe

Lightning strikes to aerospace vehicles occur mainly in or near clouds. As the Galileo entry probe will pass most of its operational life in the clouds of Jupiter, which is known to have lightning activity, the present study is concerned with the risk of a lightning strike to the probe. A strike to the probe could cause physical damage to the structure and/or damage to the electronic equipment aboard the probe. It is thought to be possible, for instance, that the instrument failures which occurred on all four Pioneer Venus entry probes at an altitude of 12 km were due to an external electric discharge. The probability of a lightning strike to the Galileo probe is evaluated. It is found that the estimate of a strike to the probe is only 0.001, which is about the same as the expected failure rate due to other design factors. In the case of entry probes to cloud-covered planets, a consideration of measures for protecting the vehicle and its payload from lightning appears to be appropriate.

Borucki, W. J.↗
NASA NTRS2011

Estimating Controller Intervention Probabilities for Optimized Profile Descent Arrivals

Simulations of arrival traffic at Dallas/Fort-Worth and Denver airports were conducted to evaluate incorporating scheduling and separation constraints into advisories that define continuous descent approaches. The goal was to reduce the number of controller interventions required to ensure flights maintain minimum separation distances of 5 nmi horizontally and 1000 ft vertically. It was shown that simply incorporating arrival meter fix crossing-time constraints into the advisory generation could eliminate over half of the all predicted separation violations and more than 80% of the predicted violations between two arrival flights. Predicted separation violations between arrivals and non-arrivals were 32% of all predicted separation violations at Denver and 41% at Dallas/Fort-Worth. A probabilistic analysis of meter fix crossing-time errors is included which shows that some controller interventions will still be required even when the predicted crossing-times of the advisories are set to add a 1 or 2 nmi buffer above the minimum in-trail separation of 5 nmi. The 2 nmi buffer was shown to increase average flight delays by up to 30 sec when compared to the 1 nmi buffer, but it only resulted in a maximum decrease in average arrival throughput of one flight per hour.

continous descent arrivals↗
DOE OSTI2023

Uniform-in-phase-space data selection with iterative normalizing flows

Improvements in computational and experimental capabilities are rapidly increasing the amount of scientific data that are routinely generated. In applications that are constrained by memory and computational intensity, excessively large datasets may hinder scientific discovery, making data reduction a critical component of data-driven methods. Datasets are growing in two directions: the number of data points and their dimensionality. Whereas dimension reduction typically aims at describing each data sample on lower-dimensional space, the focus here is on reducing the number of data points. A strategy is proposed to select data points such that they uniformly span the phase-space of the data. The algorithm proposed relies on estimating the probability map of the data and using it to construct an acceptance probability. An iterative method is used to accurately estimate the probability of the rare data points when only a small subset of the dataset is used to construct the probability map. Instead of binning the phase-space to estimate the probability map, its functional form is approximated with a normalizing flow. Therefore, the method naturally extends to high-dimensional datasets. The proposed framework is demonstrated as a viable pathway to enable data-efficient machine learning when abundant data are available.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Rare Event Sampling Improves Mercury Instability Statistics

Due to the chaotic nature of planetary dynamics, there is a non-zero probability that Mercury's orbit will become unstable in the future. Previous efforts have estimated the probability of this happening between 3 and 5 billion years in the future using a large number of direct numerical simulations with an N-body code, but were not able to obtain accurate estimates before 3 billion years in the future because Mercury instability events are too rare. In this paper we use a new rare-event sampling technique, Quantile Diffusion Monte Carlo (QDMC), to estimate that the probability of a Mercury instability event in the next 2 billion years is approximately 10 –4 in the REBOUND N-body code. We show that QDMC provides unbiased probability estimates at a computational cost of up to 100 times less than direct numerical simulation. QDMC is easy to implement and could be applied to many problems in planetary dynamics in which it is necessary to estimate the probability of a rare event.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1980

Maximum likelihood estimation of label imperfection probabilities and its use in the identification of mislabeled patterns

Estimating label imperfections and the use of estimations in the identification of mislabeled patterns are discussed. Expressions are presented for the asymptotic variances of the probability of correct classification and proportion, and for the maximum likelihood estimates of classification errors and a priori probabilities. Models are developed for imperfections in the labels and classification errors, and expressions are derived for the probability of imperfect label identification schemes resulting in wrong decisions. The expressions are used in computing thresholds and the techniques are given practical applications. The imperfect label identification scheme in the multiclass case is found to amount to establishing a region around each decision surface, and decisions of the label correction scheme are found in close agreement with the analyst-interpreter interpretations of the imagery films. As an example, the application of the maximum likelihood estimation to the processing of Landsat MSS data is discussed.

Chittineni, C. B.↗
NASA NTRS1993

On the use of the noncentral chi-square density function for the distribution of helicopter spectral estimates

A probability density function for the variability of ensemble averaged spectral estimates from helicopter acoustic signals in Gaussian background noise was evaluated. Numerical methods for calculating the density function and for determining confidence limits were explored. Density functions were predicted for both synthesized and experimental data and compared with observed spectral estimate variability.

Garber, Donald P.↗
NASA NTRS1998

Lightning Characteristics and Lightning Strike Peak Current Probabilities as Related to Aerospace Vehicle Operations

A summary is presented of basic lightning characteristics/criteria for current and future NASA aerospace vehicles. The paper estimates the probability of occurrence of a 200 kA peak lightning return current, should lightning strike an aerospace vehicle in various operational phases, i.e., roll-out, on-pad, launch, reenter/land, and return-to-launch site. A literature search was conducted for previous work concerning occurrence and measurement of peak lighting currents, modeling, and estimating probabilities of launch vehicles/objects being struck by lightning. This paper presents these results.

Johnson, Dale L.↗
NASA NTRS2008

Approximation of Failure Probability Using Conditional Sampling

In analyzing systems which depend on uncertain parameters, one technique is to partition the uncertain parameter domain into a failure set and its complement, and judge the quality of the system by estimating the probability of failure. If this is done by a sampling technique such as Monte Carlo and the probability of failure is small, accurate approximation can require so many sample points that the computational expense is prohibitive. Previous work of the authors has shown how to bound the failure event by sets of such simple geometry that their probabilities can be calculated analytically. In this paper, it is shown how to make use of these failure bounding sets and conditional sampling within them to substantially reduce the computational burden of approximating failure probability. It is also shown how the use of these sampling techniques improves the confidence intervals for the failure probability estimate for a given number of sample points and how they reduce the number of sample point analyses needed to achieve a given level of confidence.

Giesy. Daniel P.↗