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Gradient estimation for stochastic optimization of optical code-division multiple-access systems .I. Generalized sensitivity analysis

N.B. Mandayam, B. Aazhang
1997 IEEE Journal on Selected Areas in Communications  
Specifically, computer-aided techniques such as infinitesimal perturbation analysis (IPA) and likelihood ratio (LR) methods are used for analyzing the sensitivity of the average BER to a wide class of  ...  Index Terms-Discrete-event simulations, frequency-encoded OCDMA, infinitesimal perturbation analysis, likelihood ratio method, time-encoded OCDMA.  ...  Characterization of Probability Density Functions for Likelihood Ratio Method We now present a theorem that characterizes probability density functions for which the likelihood ratio method of sensitivity  ... 
doi:10.1109/49.585783 fatcat:fqlwnottfzd6bonxjwrc4vwygq

Maximum likelihood calibration of stochastic multipath radio channel models

Christian Hirsch, Ayush Bharti, Troels Pedersen, Rasmus Waagepetersen
2020 IEEE Transactions on Antennas and Propagation  
We propose Monte Carlo maximum likelihood estimation as a novel approach in the context of calibration and selection of stochastic channel models.  ...  Then, we illustrate the advantages and pitfalls of the method on the basis of simulated data. Finally, we apply our calibration method to wideband signal data from indoor channels.  ...  In this paper, we propose to use the principled and recognized statistical methodology of maximum likelihood estimation (MLE) to calibrate stochastic channel models with inhomogeneous intensity function  ... 
doi:10.1109/tap.2020.3044379 fatcat:bnkvkp7z6vflvcaebavxpmlwze

Pricing and hedging American-style options: a simple simulation-based approach

Yang Wang, Russel Caflisch
2010 Journal of Computational Finance  
Our approach is intuitive, easy to apply, computationally efficient and, most importantly, provides a unified framework for estimating risk sensitivities of the option price to underlying spot prices.  ...  analytically to generate estimates for the Greeks.  ...  To further compare the MLSM method with other existing simulationbased methods for computing Greeks, we also report estimates of deltas from the pathwise derivative method and the likelihood ratio method  ... 
doi:10.21314/jcf.2010.220 fatcat:nrl3kat645hchgmculepoqwtsy

Local Riemannian geometry of model manifolds and its implications for practical parameter identifiability

Daniel Lill, Jens Timmer, Daniel Kaschek, Timon Idema
2019 PLoS ONE  
This situation, denoted as practical non-identifiability, can be detected by Monte Carlo sampling or by systematic scanning using the profile likelihood method.  ...  The Christoffel Symbols in the geodesic equation are fixed to those obtained from second order model sensitivities at the optimum.  ...  Introduction Parameter estimation by the maximum-likelihood method has numerous applications in different fields of physics, engineering, and other quantitative sciences.  ... 
doi:10.1371/journal.pone.0217837 pmid:31158252 pmcid:PMC6546239 fatcat:qjztodkbsjcpdlnwt57bn3a3mq

Computing Bayes Factors Using Thermodynamic Integration

Nicolas Lartillot, Hervé Philippe, Paul Lewis
2006 Systematic Biology  
In recent phylogenetic works, the numerical evaluation of marginal likelihoods has often been performed using the harmonic mean estimation procedure.  ...  In the Bayesian paradigm, a common method for comparing two models is to compute the Bayes factor, defined as the ratio of their respective marginal likelihoods.  ...  We are also grateful to Nicolas Rodrigue, David Bryant, Olivier Gascuel, Thomas Lepage, and the two referees for their useful comments on the work and on the manuscript.  ... 
doi:10.1080/10635150500433722 pmid:16522570 fatcat:iw6dunyexrcxxdso7pvqeakyoe

Marginal likelihoods in phylogenetics: a review of methods and applications [article]

Jamie R. Oaks, Kerry A. Cobb, Vladimir N. Minin, Adam D. Leaché
2019 arXiv   pre-print
We also categorize and review methods for estimating marginal likelihoods of phylogenetic models, highlighting several recent methods that provide well-behaved estimates.  ...  Using simulations, we find one alternative method based on approximate-Bayesian computation (ABC) to be biased.  ...  They used path-sampling and stepping-stone methods to estimate the marginal likelihoods of strict and relaxed-clock models for sequence data of herpes viruses.  ... 
arXiv:1805.04072v3 fatcat:6da22zuymrfzvmhlrnonzolepu

Making Steppingstones out of Stumbling Blocks: A Bayesian Model Evidence Estimator with Application to Groundwater Transport Model Selection

Elshall, Ye
2019 Water  
We also consider the path sampling methods of thermodynamic integration (TI) and steppingstone sampling (SS) that sample multiple intermediate distributions that link the prior and the posterior.  ...  Thus, biased BME estimation results in inaccurate penalization of more complex models, which changes the model ranking. This was less observed with SS and MOSS as with the three other methods.  ...  The data and computer codes used to produce this paper are available by contacting the corresponding author at mye@fsu.edu.  ... 
doi:10.3390/w11081579 fatcat:qms7heo2ynejlnsjdt5lrgu5eq

Uncertainty quantification for generalized Langevin dynamics

Eric J. Hall, Markos A. Katsoulakis, Luc Rey-Bellet
2016 Journal of Chemical Physics  
In particular, we apply these estimators to analyze an extended variable formulation of the GLE where other well known sensitivity analysis techniques such as the likelihood ratio method are not applicable  ...  the difference estimator relative to sampling dynamics driven by independent paths.  ...  principle the Malliavin method applies to other perturbations that cannot be handled by pathwise and likelihood ratio methods, it requires a number of auxiliary processes that may scale poorly with the  ... 
doi:10.1063/1.4971433 pmid:27984878 fatcat:zsw3euopw5cqfc5kz5tj357vfm

Principle of structural equation modeling for exploring functional interactivity within a putative network of interconnected brain areas

Giovanni de Marco, Pierre Vrignaud, Christophe Destrieux, Damien de Marco, Sylvie Testelin, Bernard Devauchelle, Patrick Berquin
2009 Magnetic Resonance Imaging  
When applied to the field of neurosciences, structural equation modeling (SEM) uses theoretical and/or empirical hypotheses to estimate the effects of an experimental task within a putative network.  ...  SEM represents a linear technique for multivariate analysis of neuroimaging data and has been developed to simultaneously examine ratios of multiple causality in an experimental design; the method attempts  ...  However, maximum Lagrangian multipliers are sensitive to the sample size; changes are more likely to be significant with large samples.  ... 
doi:10.1016/j.mri.2008.05.003 pmid:18584986 fatcat:7tllqdxg5jdhfmoldrhj5je274

Stochastic averaging and sensitivity analysis for two scale reaction networks

Araz Hashemi, Marcel Núñez, Petr Plecháč, Dionisios G. Vlachos
2016 Journal of Chemical Physics  
In particular, we propose a new lower-variance ergodic likelihood ratio type estimator for steady-state estimation and show how one can adapt it to accelerated simulations of multiscale systems.Lastly,  ...  This work presents stochastic averaging techniques to accelerate computations for obtaining estimates of expected values and sensitivities with respect to the steady state distribution.  ...  Motivated by the variance reduction one obtains with ergodic averaging, we introduce a new method for computing likelihood-ratio type steady state sensitivity estimates.  ... 
doi:10.1063/1.4942008 pmid:26896973 fatcat:drunqbwojjbqtb22nwmtpzc654

Introduction to Structural Equation Modeling: Issues and Practical Considerations

Pui-Wa Lei, Qiong Wu
2007 Educational Measurement: Issues and Practice  
Its estimation techniques, modeling capacities, and breadth of applications are expanding rapidly. This module introduces some common terminologies.  ...  In addition, several popular specialized SEM software programs are briefly discussed with regard to their features and availability.  ...  Acknowledgment We thank James DiPerna, Paul Morgan, and Marley Watkins for sharing the data used in the illustrative examples, as well as Hoi Suen and the anonymous reviewers for their constructive comments  ... 
doi:10.1111/j.1745-3992.2007.00099.x fatcat:c5ujafmxkfgizkjxqbxt5tq6tq

Calibrated path sampling and stepwise bridge sampling

Zhiqiang Tan
2013 Journal of Statistical Planning and Inference  
Path sampling seems a compromise, using samples along a 1-dimensional path to compute each integral.  ...  There are various methods using a single sample only or multiple samples jointly to compute each integral.  ...  There are two subtle limitations for path-sampling methods considered here. Each single-path estimator on a fixed grid is in general inconsistent and so is the calibrated path sampling estimator.  ... 
doi:10.1016/j.jspi.2012.10.001 fatcat:z57kwma44jfh3asubz6xdzpxbm

On the Potential of 3D Transdimensional Surface Wave Tomography for Geothermal Prospecting of the Reykjanes Peninsula

Amin Rahimi Rahimi Dalkhani, Xin Zhang, Cornelis Weemstra
2021 Remote Sensing  
The algorithm is rooted in a Bayesian framework using Markov chains with reversible jumps, and is referred to as transdimensional tomography.  ...  At the same time, the velocity structure is less well recovered in parts of the peninsula sampled by fewer stations.  ...  [20] used fixed ray paths to compute travel times and updated the ray paths only occasionally (three times for three million samples). In this way, they linearized the algorithm partially.  ... 
doi:10.3390/rs13234929 fatcat:vcvcubpaqbbwpa7q3qjd35xgu4

Estimation of Received Signal Strength Distribution for Smart Meters with Biased Measurement Data Set

Mathias R. Kielgast, Anders C. Rasmussen, Mathias H. Laursen, Jimmy J. Nielsen, Petar Popovski, Rasmus Krigslund
2016 IEEE Wireless Communications Letters  
It is shown that the proposed method offers an approximation of the distribution of the signal strength measurements that is better than a na\"ive Rician fitting.  ...  We combine a Rician fading model with a bias function that captures the cut-off in the observed signal strength measurements. Two sets of experimental data are analysed.  ...  Numerous methods for estimation of the Rician K-factor exist, both using maximum likelihood methods [3] , as well as moment based methods [4] , [5] ; however, none of these methods take the measurement  ... 
doi:10.1109/lwc.2016.2619346 fatcat:oqniwdefgvawpjwhoxucpdpkqy

Fair Inference on Outcomes

Razieh Nabi, Ilya Shpitser
2018 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Examples include classification and regression problems, and estimating treatment effects in randomized trials or observational data.  ...  The issue of fairness arises in such problems where some covariates or treatments are "sensitive," in the sense of having potential of creating discrimination.  ...  Using the IPW estimator, which only uses A and M models, we obtain the NDE (on the ratio scale) of 3.01.  ... 
pmid:29796336 pmcid:PMC5963284 fatcat:3zsudurrhbfebndti3nsnk3kmi
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