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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Probing Anisotropic Cosmic Birefringence with Foreground-Marginalised SPT B-mode Likelihoods

In this work, we construct foreground-marginalised versions of the SPT-3G D1 and SPTpol cosmic microwave background (CMB) B-mode polarisation likelihoods. The compression is performed using the CMB-lite framework and we use the resulting data sets to constrain anisotropic cosmic birefringence, parametrised by the amplitude of a scale-invariant anisotropic birefringence spectrum, A CB . Using the new SPT-3G data we report a upper limit on of 95% upper limit on A CB of 1.2 x 10 -4 , which tightens to 0.53 x 10 -4 when imposing a prior on the amplitude of gravitational lensing based on CMB lensing reconstruction analyses. These are the tightest constraints on anisotropic birefringence from BB power spectrum measurements to-date, demonstrating the constraining power of the South Pole Telescope. The likelihoods used in this work are made publicly available at https://github.com/lbalkenhol/candl_data

Balkenhol, Lennart [Sorbonne Université, Paris (Fr↗
NASA NTRS1968

Maximum Likelihood Properties

Maximum likelihood properties and asymptotic distributions for limiting distribution of likelihood ratio statistic under class of local alternatives

Lever, W. E.↗
NASA NTRS1972

Longitudinal stability and control derivatives of a jet fighter airplane extracted from flight test data by utilizing maximum likelihood estimation

A method of parameter extraction for stability and control derivatives of aircraft from flight test data, implementing maximum likelihood estimation, was developed and successfully applied to actual longitudinal flight test data from a modern sophisticated jet fighter. The results of this application establish the merits of the estimation technique and its computer implementation(allowing full analyst interaction with the program) as well as provide data for the validation of a portion of the differential maneuvering simulator (DMS). The results are presented for all flight test runs in tabular form and as time history comparisons between the estimated states and the actual flight test data. Comparisons between extracted and manufacturer's values for five major derivatives are presented and reveal good agreement for these principal derivatives with one exception. This particular derivative is extensively investigated by utilizing the interactive capabilities of the computer program. The results of this investigation verify the numbers extracted by maximum likelihood estimation.

Steinmetz, G. G.↗
NASA NTRS1973

Maximum likelihood classification by thresholding

The standard maximum-likelihood classifier is reformulated so that, in most cases, only a small number of density functions need be computed each time a data point is to be classified. The technique relies upon class thresholds which are obtained at the beginning of the classification process and which remain fixed thereafter. The result of the reformulation is that a significant reduction in classification processing time is obtained while retaining complete consistency with the standard maximum-likelihood classifier.

Minter, T. C.↗
NASA NTRS1975

Maximum likelihood signature estimation

Maximum-likelihood estimates are discussed which are based on an unlabeled sample of observations, of unknown parameters in a mixture of normal distributions. Several successive approximation procedures for obtaining such maximum-likelihood estimates are described. These procedures, which are theoretically justified by the local contractibility of certain maps, are designed to take advantage of good initial estimates of the unknown parameters. They can be applied to the signature extension problem, in which good initial estimates of the unknown parameters are obtained from segments which are geographically near the segments from which the unlabeled samples are taken. Additional problems to which these methods are applicable include: estimation of proportions and adaptive classification (estimation of mean signatures and covariances).

Walker, H. F.↗
NASA NTRS1974

Computational aspects of maximum likelihood estimation and reduction in sensitivity function calculations

This paper discusses numerical aspects of computing maximum likelihood estimates for linear dynamical systems in state-vector form. Different gradient-based nonlinear programming methods are discussed in a unified framework and their applicability to maximum likelihood estimation is examined. The problems due to singular Hessian or singular information matrix that are common in practice are discussed in detail and methods for their solution are proposed. New results on the calculation of state sensitivity functions via reduced order models are given. Several methods for speeding convergence and reducing computation time are also discussed.

Gupta, N. K.↗
NASA NTRS1975

An iterative procedure for obtaining maximum-likelihood estimates of the parameters for a mixture of normal distributions, Addendum

New results and insights concerning a previously published iterative procedure for obtaining maximum-likelihood estimates of the parameters for a mixture of normal distributions were discussed. It was shown that the procedure converges locally to the consistent maximum likelihood estimate as long as a specified parameter is bounded between two limits. Bound values were given to yield optimal local convergence.

Peters, B. C., Jr.↗
NASA NTRS1976

The recursive maximum likelihood proportion estimator: User's guide and test results

Implementation of the recursive maximum likelihood proportion estimator is described. A user's guide to programs as they currently exist on the IBM 360/67 at LARS, Purdue is included, and test results on LANDSAT data are described. On Hill County data, the algorithm yields results comparable to the standard maximum likelihood proportion estimator.

Vanrooy, D. L.↗
NASA NTRS1975

A note on the consistency of maximum likelihood estimates for finite families of stochastic processes

The note considers families of stochastic processes indexed by a finite number of alternative parameter values. For general classes of stochastic processes, it is shown that maximum-likelihood estimates converge almost surely to the correct parameter value. This is established by use of a submartingale property of the sequence of maximized-likelihood ratios together with a technique first employed by Wald (1949) in the case of independent identically distributed random variables.

Caines, P. E.↗
NASA NTRS1975

Regions of constrained maximum likelihood parameter identifiability

This paper considers the parameter identification problem of general discrete-time, nonlinear, multiple-input/multiple-output dynamic systems with Gaussian-white distributed measurement errors. Knowledge of the system parameterization is assumed to be known. Regions of constrained maximum likelihood (CML) parameter identifiability are established. A computation procedure employing interval arithmetic is proposed for finding explicit regions of parameter identifiability for the case of linear systems. It is shown that if the vector of true parameters is locally CML identifiable, then with probability one, the vector of true parameters is a unique maximal point of the maximum likelihood function in the region of parameter identifiability and the CML estimation sequence will converge to the true parameters.

Lee, C.-H.↗
NASA NTRS1976

Maximum likelihood identifier refinements for F-8C adaptive control

Design refinement is described for baseline F-8C adaptive flight test control laws which consist of simplified quadratic-optimal model-following control structures in the pitch and lateral directional axes. The variable gains of both axes are adjusted adaptively by an explicit on-line maximum likelihood parameter identifier. The identifier, consisting of five parallel computing channels which evaluate the likelihood functions at discrete points in parameter space, operates in pitch only, using pitch rate, normal acceleration, and elevator position measurements.

Stein, G.↗
NASA NTRS1977

The Maximum Likelihood Estimation of Signature Transformation /MLEST/ algorithm

The Maximum Likelihood Estimation of Signature Transformation (MLEST) algorithm is used to obtain maximum likelihood estimates (MLE) of affine transformation. The algorithm has been evaluated for three sets of data: simulated (training and recognition segment pairs), consecutive-day (data gathered from Landsat images), and geographical-extension (large-area crop inventory experiment) data sets. For each set, MLEST signature extension runs were made to determine MLE values and the affine-transformed training segment signatures were used to classify the recognition segments. The classification results were used to estimate wheat proportions at 0 and 1% threshold values.

Thadani, S. G.↗
NASA NTRS1979

Parameter estimation in astronomy through application of the likelihood ratio

Many problems in the experimental estimation of parameters for models can be solved through use of the likelihood ratio test. Applications of the likelihood ratio, with particular attention to photon counting experiments, are discussed. The procedures presented solve a greater range of problems than those currently in use, yet are no more difficult to apply. The procedures are proved analytically, and examples from current problems in astronomy are discussed.

Cash, W.↗
NASA NTRS1979

Parameter estimation in X-ray astronomy using maximum likelihood

Methods of estimation of parameter values and confidence regions by maximum likelihood and Fisher efficient scores starting from Poisson probabilities are developed for the nonlinear spectral functions commonly encountered in X-ray astronomy. It is argued that these methods offer significant advantages over the commonly used alternatives called minimum chi-squared because they rely on less pervasive statistical approximations and so may be expected to remain valid for data of poorer quality. Extensive numerical simulations of the maximum likelihood method are reported which verify that the best-fit parameter value and confidence region calculations are correct over a wide range of input spectra.

Wachter, K.↗
NASA NTRS1979

Maximum likelihood estimation for life distributions with competing failure modes

The general model for the competing failure modes assuming that location parameters for each mode are expressible as linear functions of the stress variables and the failure modes act independently is presented. The general form of the likelihood function and the likelihood equations are derived for the extreme value distributions, and solving these equations using nonlinear least squares techniques provides an estimate of the asymptotic covariance matrix of the estimators. Monte-Carlo results indicate that, under appropriate conditions, the location parameters are nearly unbiased, the scale parameter is slightly biased, and the asymptotic covariances are rapidly approached.

Sidik, S. M.↗