Relevant bibliographies by topics / Variance reduction

Academic literature on the topic 'Variance reduction'

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Published: 4 June 2021
Last updated: 7 September 2024

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Journal articles on the topic "Variance reduction"

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James, B. A. P. "Variance Reduction Techniques." Journal of the Operational Research Society 36, no. 6 (June 1985): 525. http://dx.doi.org/10.2307/2582825.

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Rouhani, Shahrokh. "Variance Reduction Analysis." Water Resources Research 21, no. 6 (June 1985): 837–46. http://dx.doi.org/10.1029/wr021i006p00837.

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James, B. A. P. "Variance Reduction Techniques." Journal of the Operational Research Society 36, no. 6 (June 1985): 525–30. http://dx.doi.org/10.1057/jors.1985.88.

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Tjärnström, F., and L. Ljung. "L2 Model reduction and variance reduction." Automatica 38, no. 9 (September 2002): 1517–30. http://dx.doi.org/10.1016/s0005-1098(02)00066-3.

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Hwang, Chii-Ruey, Raoul Normand, and Sheng-Jhih Wu. "Variance reduction for diffusions." Stochastic Processes and their Applications 125, no. 9 (September 2015): 3522–40. http://dx.doi.org/10.1016/j.spa.2015.03.006.

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Tjärnström, Fredrik, and Lennart Ljung. "L 2 Model Reduction and Variance Reduction." IFAC Proceedings Volumes 33, no. 15 (June 2000): 929–34. http://dx.doi.org/10.1016/s1474-6670(17)39872-5.

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Newton, Nigel J. "Variance Reduction for Simulated Diffusions." SIAM Journal on Applied Mathematics 54, no. 6 (December 1994): 1780–805. http://dx.doi.org/10.1137/s0036139992236220.

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L'Ecuyer, Pierre, and Christiane Lemieux. "Variance Reduction via Lattice Rules." Management Science 46, no. 9 (September 2000): 1214–35. http://dx.doi.org/10.1287/mnsc.46.9.1214.12231.

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Berlinet, Alain. "A note on variance reduction." Statistics & Probability Letters 25, no. 4 (December 1995): 357–60. http://dx.doi.org/10.1016/0167-7152(94)00241-5.

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Lynch, Thomas B. "Variance Reduction for Sector Sampling." Forest Science 52, no. 3 (June 1, 2006): 251–61. http://dx.doi.org/10.1093/forestscience/52.3.251.

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Abstract Variance reduction techniques used in Monte Carlo integration including control variates and importance sampling can use estimated or actual shapes of vegetated land areas to reduce the variance of estimators from sector sampling. The estimated shapes of forested areas could come from maps, aerial photos, or similar sources. Antithetic variates for variance reduction in Monte Carlo integration can be applied to sector sampling without using any estimated shape or map. Sector sampling selects as samples all trees or other vegetation of interest located in randomly chosen sectors that have a vertex at a common point located in the interior of a forested area. Each sector is associated with an angle of fixed magnitude emanating from the interior point. Sector orientation is based on a randomly selected azimuth originating at the interior point that is the vertex for each sector. This technique is well adapted to application on relatively small areas that have irregularly shaped boundaries. Sector sampling can be demonstrated to be unbiased. Unbiasedness holds with angle reduction for adjustment of sample size and with the variance reduction techniques that are proposed. The method is not restricted to application with trees because sector sampling methods could be applied just as well to other vegetation types or any other objects located in the land area of interest.
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Dissertations / Theses on the topic "Variance reduction"

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Chouinard, Hayley Helene. "Reduction of yield variance through crop insurance." Thesis, Montana State University, 1994. http://etd.lib.montana.edu/etd/1994/chouinard/ChouinardH1994.pdf.

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The variance of a producer's yield provides uncertainty and may be considered the risk a producer faces. crop insurance may provide protection against yield variability. If yields are necessarily low, an insured producer may receive an indemnity payment. Currently, crop insurance is based on each individual's yield. If the individual's yield falls below a specified level, the individual will receive an indemnity. An alternative crop insurance program bases indemnities on . an area yield. If the yield of the predetermined area falls below a specific level, all insured producers will receive an indemnity. This thesis examines the yield variability reduction received by purchasing various forms of area yield and individual yield crop insurance and the actuarially fair premium costs associated with them. When a producer purchases insurance two decisions are made. First, the producer selects a trigger level which determines the critical yield which generates an indemnity payment. Second, the producer may be able to select a coverage level which is the amount of acreage covered by the contract. Each contract examined allows different levels for the trigger and coverage levels. The variance reduction provided from each contract is the variance of the yield without insurance less the variance of the yield with an insurance contract. The results indicate most producers receive some variance reduction from the area yield contracts. And, producers who have yields which are closely correlated with the area yield receive more variance reduction from the area yield insurance than from the individual yield insurance contracts. However, the area yield contracts which provide on average more yield variance reduction than the individual yield contracts, also have much higher actuarially fair premium costs. The area yield insurance contracts should be considered as an alternative to individual yield insurance, but the premium costs must be evaluated also.
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Gagakuma, Bertelsen. "Variance Reduction in Wind Farm Layout Optimization." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7758.

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As demand for wind power continues to grow, it is becoming increasingly important to minimize the risk, characterized by the variance, that is associated with long-term power forecasts. This thesis investigated variance reduction in power forecasts from wind farm layout optimization.The problem was formulated as a multi-objective optimization one of maximizing mean-plant-power and minimizing variance. The ε−constraint method was used to solve the bi-objectiveproblem in a two-step optimization framework where two sequential optimizations are performed. The first is maximizing mean wind farm power alone and the second, minimizing variance with a constraint on the mean power which is the value from the first optimization. The results show that the variance in power estimates can be reduced by up to 30%, without sacrificing mean-plant-power for the different farm sizes and wind conditions studied. This reduction is attributed to the multi-modality of the design space which allows for unique solutions of high mean plant power at different power variances. Thus, wind farms can be designed to maximize power capture with greater confidence.
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Greensmith, Evan, and evan greensmith@gmail com. "Policy Gradient Methods: Variance Reduction and Stochastic Convergence." The Australian National University. Research School of Information Sciences and Engineering, 2005. http://thesis.anu.edu.au./public/adt-ANU20060106.193712.

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In a reinforcement learning task an agent must learn a policy for performing actions so as to perform well in a given environment. Policy gradient methods consider a parameterized class of policies, and using a policy from the class, and a trajectory through the environment taken by the agent using this policy, estimate the performance of the policy with respect to the parameters. Policy gradient methods avoid some of the problems of value function methods, such as policy degradation, where inaccuracy in the value function leads to the choice of a poor policy. However, the estimates produced by policy gradient methods can have high variance.¶ In Part I of this thesis we study the estimation variance of policy gradient algorithms, in particular, when augmenting the estimate with a baseline, a common method for reducing estimation variance, and when using actor-critic methods. A baseline adjusts the reward signal supplied by the environment, and can be used to reduce the variance of a policy gradient estimate without adding any bias. We find the baseline that minimizes the variance. We also consider the class of constant baselines, and find the constant baseline that minimizes the variance. We compare this to the common technique of adjusting the rewards by an estimate of the performance measure. Actor-critic methods usually attempt to learn a value function accurate enough to be used in a gradient estimate without adding much bias. In this thesis we propose that in learning the value function we should also consider the variance. We show how considering the variance of the gradient estimate when learning a value function can be beneficial, and we introduce a new optimization criterion for selecting a value function.¶ In Part II of this thesis we consider online versions of policy gradient algorithms, where we update our policy for selecting actions at each step in time, and study the convergence of the these online algorithms. For such online gradient-based algorithms, convergence results aim to show that the gradient of the performance measure approaches zero. Such a result has been shown for an algorithm which is based on observing trajectories between visits to a special state of the environment. However, the algorithm is not suitable in a partially observable setting, where we are unable to access the full state of the environment, and its variance depends on the time between visits to the special state, which may be large even when only few samples are needed to estimate the gradient. To date, convergence results for algorithms that do not rely on a special state are weaker. We show that, for a certain algorithm that does not rely on a special state, the gradient of the performance measure approaches zero. We show that this continues to hold when using certain baseline algorithms suggested by the results of Part I.
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Tjärnström, Fredrik. "Variance expressions and model reduction in system identification /." Linköping : Univ, 2002. http://www.bibl.liu.se/liupubl/disp/disp2002/tek730s.pdf.

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Wise, Michael Anthony. "A variance reduction technique for production cost simulation." Ohio : Ohio University, 1989. http://www.ohiolink.edu/etd/view.cgi?ohiou1182181023.

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Rowland, Kelly L. "Advanced Quadrature Selection for Monte Carlo Variance Reduction." Thesis, University of California, Berkeley, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10817512.

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Neutral particle radiation transport simulations are critical for radiation shielding and deep penetration applications. Arriving at a solution for a given response of interest can be computationally difficult because of the magnitude of particle attenuation often seen in these shielding problems. Hybrid methods, which aim to synergize the individual favorable aspects of deterministic and stochastic solution methods for solving the steady-state neutron transport equation, are commonly used in radiation shielding applications to achieve statistically meaningful results in a reduced amount of computational time and effort. The current state of the art in hybrid calculations is the Consistent Adjoint-Driven Importance Sampling (CADIS) and Forward-Weighted CADIS (FW-CADIS) methods, which generate Monte Carlo variance reduction parameters based on deterministically-calculated scalar flux solutions. For certain types of radiation shielding problems, however, results produced using these methods suffer from unphysical oscillations in scalar flux solutions that are a product of angular discretization. These aberrations are termed “ray effects”.

The Lagrange Discrete Ordinates (LDO) equations retain the formal structure of the traditional discrete ordinates formulation of the neutron transport equation and mitigate ray effects at high angular resolution. In this work, the LDO equations have been implemented in the Exnihilo parallel neutral particle radiation transport framework, with the deterministic scalar flux solutions passed to the Automated Variance Reduction Generator (ADVANTG) software and the resultant Monte Carlo variance reduction parameters’ efficacy assessed based on results from MCNP5. Studies were conducted in both the CADIS and FW-CADIS contexts, with the LDO equations’ variance reduction parameters seeing their best performance in the FW-CADIS method, especially for photon transport.

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Greensmith, Evan. "Policy gradient methods : variance reduction and stochastic convergence /." View thesis entry in Australian Digital Theses Program, 2005. http://thesis.anu.edu.au/public/adt-ANU20060106.193712/index.html.

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Yang, Yani. "Dimension reduction in the regressions through weighted variance estimation." HKBU Institutional Repository, 2009. http://repository.hkbu.edu.hk/etd_ra/1073.

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Höök, Lars Josef. "Variance reduction methods for numerical solution of plasma kinetic diffusion." Licentiate thesis, KTH, Fusionsplasmafysik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-91332.

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Performing detailed simulations of plasma kinetic diffusion is a challenging task and currently requires the largest computational facilities in the world. The reason for this is that, the physics in a confined heated plasma occur on a broad range of temporal and spatial scales. It is therefore of interest to improve the computational algorithms together with the development of more powerful computational resources. Kinetic diffusion processes in plasmas are commonly simulated with the Monte Carlo method, where a discrete set of particles are sampled from a distribution function and advanced in a Lagrangian frame according to a set of stochastic differential equations. The Monte Carlo method introduces computational error in the form of statistical random noise produced by a finite number of particles (or markers) N and the error scales as αN−β where β = 1/2 for the standard Monte Carlo method. This requires a large number of simulated particles in order to obtain a sufficiently low numerical noise level. Therefore it is essential to use techniques that reduce the numerical noise. Such methods are commonly called variance reduction methods. In this thesis, we have developed new variance reduction methods with application to plasma kinetic diffusion. The methods are suitable for simulation of RF-heating and transport, but are not limited to these types of problems. We have derived a novel variance reduction method that minimizes the number of required particles from an optimization model. This implicitly reduces the variance when calculating the expected value of the distribution, since for a fixed error the  optimization model ensures that a minimal number of particles are needed. Techniques that reduce the noise by improving the order of convergence, have also been considered. Two different methods have been tested on a neutral beam injection scenario. The methods are the scrambled Brownian bridge method and a method here called the sorting and mixing method of L´ecot and Khettabi[1999]. Both methods converge faster than the standard Monte Carlo method for modest number of time steps, but fail to converge correctly for large number of time steps, a range required for detailed plasma kinetic simulations. Different techniques are discussed that have the potential of improving the convergence to this range of time steps.
QC 20120314
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Aghedo, Maurice Enoghayinagbon. "Variance reduction in Monte Carlo methods of estimating distribution functions." Thesis, Imperial College London, 1985. http://hdl.handle.net/10044/1/37385.

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Books on the topic "Variance reduction"

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Mahmood, M. A. Variance reduction techniques in the simulation of queueingproblems. Manchester: UMIST, 1986.

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Lewis, Peter A. W. Variance reduction for quantile estimates in simulations via nonlinear controls. Monterey, Calif: Naval Postgraduate School, 1990.

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Lessi, O. Problems of variance reduction in the simulation of random variables. Luxembourg: Commission of the European Communities, 1987.

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Trietsch, Dan. A proposal to apply Taguchi-Inspired Methods to the reduction of machining variance. Monterey, Calif: Naval Postgraduate School, 1992.

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Ressler, Richard L. An investigation of nonlinear controls and regression-adjusted estimators for variance reduction in computer simulation. Monterey, Calif: Naval Postgraduate School, 1991.

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Mackenzie, Anne I. Three averaging techniques for reduction of antenna temperature variance measured by a Dicke mode, C-band radiometer. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 2000.

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Smoot, Melissa C. An analysis of noise reduction in variable reluctance motors using pulse position randomization. Springfield, Va: Available from National Technical Information Service, 1994.

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Brausch, John F. Simulated flight acoustic investigtion of treated ejector effectiveness on advanced mechanical suppressors for high velocity jet noise reduction. Cleveland, Ohio: Lewis Research Center, 1986.

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Institute for Computer Applications in Science and Engineering., ed. Practical aspects of variable reduction formulations and reduced basis algorithms in multidisciplinary design optimization. Hampton, VA: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1995.

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Replicative Use of an External Model in Simulation Variance Reduction. Storming Media, 1996.

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Book chapters on the topic "Variance reduction"

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Bratley, Paul, Bennett L. Fox, and Linus E. Schrage. "Variance Reduction." In A Guide to Simulation, 44–76. New York, NY: Springer New York, 1987. http://dx.doi.org/10.1007/978-1-4419-8724-2_2.

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Gass, Saul I., and Carl M. Harris. "Variance reduction." In Encyclopedia of Operations Research and Management Science, 865. New York, NY: Springer US, 2001. http://dx.doi.org/10.1007/1-4020-0611-x_1102.

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Pagès, Gilles. "Variance Reduction." In Universitext, 49–94. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-90276-0_3.

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Sen, Rituparna, and Sourish Das. "Variance Reduction." In Computational Finance with R, 127–43. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-2008-0_11.

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Kloeden, Peter E., and Eckhard Platen. "Variance Reduction Methods." In Numerical Solution of Stochastic Differential Equations, 511–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-662-12616-5_16.

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Dupree, Stephen A., and Stanley K. Fraley. "Variance Reduction Techniques." In A Monte Carlo Primer, 139–74. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4419-8491-3_6.

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Platen, Eckhard, and Nicola Bruti-Liberati. "Variance Reduction Techniques." In Numerical Solution of Stochastic Differential Equations with Jumps in Finance, 637–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13694-8_16.

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Lemieux, Christiane. "Variance Reduction Techniques." In Springer Series in Statistics, 1–52. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-78165-5_4.

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Dupree, Stephen A., and Stanley K. Fraley. "Variance Reduction Techniques." In A Monte Carlo Primer, 75–108. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-1-4419-9036-5_6.

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Glasserman, Paul. "Variance Reduction Techniques." In Stochastic Modelling and Applied Probability, 185–279. New York, NY: Springer New York, 2004. http://dx.doi.org/10.1007/978-0-387-21617-1_4.

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Conference papers on the topic "Variance reduction"

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Cheng, Russell C. H. "Variance reduction methods." In the 18th conference. New York, New York, USA: ACM Press, 1986. http://dx.doi.org/10.1145/318242.318261.

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Avramidis, Athanassios N., and James R. Wilson. "Integrated variance reduction strategies." In the 25th conference. New York, New York, USA: ACM Press, 1993. http://dx.doi.org/10.1145/256563.256685.

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Nelson, Barry L. "Variance reduction for simulation practitioners." In the 19th conference. New York, New York, USA: ACM Press, 1987. http://dx.doi.org/10.1145/318371.318382.

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Nannen, Volker, and A. E. Eiben. "Variance reduction in meta-EDA." In the 9th annual conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1276958.1277087.

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Han, Chuan-Hsiang, and Yu-Tuan Lin. "Accelerated variance reduction methods on GPU." In 2014 20th IEEE International Conference on Parallel and Distributed Systems (ICPADS). IEEE, 2014. http://dx.doi.org/10.1109/padsw.2014.7097926.

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Yang, Haijun, and Sa An. "Variance Reduction Techniques for Basket Option." In 2010 International Conference on Management and Service Science (MASS 2010). IEEE, 2010. http://dx.doi.org/10.1109/icmss.2010.5578221.

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Li, Chenzhao, and Sankaran Mahadevan. "Variance Reduction Estimation in Bayesian Inference." In 19th AIAA Non-Deterministic Approaches Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2017. http://dx.doi.org/10.2514/6.2017-1772.

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Nelson, Barry L. "A decomposition approach to variance reduction." In the 17th conference. New York, New York, USA: ACM Press, 1985. http://dx.doi.org/10.1145/21850.253059.

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Decock, Jérémie, Jialin Liu, and Olivier Tetaud. "Variance Reduction in Population-Based Optimization." In GECCO '15: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2739482.2764638.

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De, Soham, and Tom Goldstein. "Efficient Distributed SGD with Variance Reduction." In 2016 IEEE 16th International Conference on Data Mining (ICDM). IEEE, 2016. http://dx.doi.org/10.1109/icdm.2016.0022.

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Reports on the topic "Variance reduction"

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Booth, Thomas E. MCNP Variance Reduction Examples. Office of Scientific and Technical Information (OSTI), October 2012. http://dx.doi.org/10.2172/1054246.

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Spencer, Joshua Bradly, Roger Lee Martz, and Jennifer Louise Alwin. Variance Reduction Introduction & Overview. Office of Scientific and Technical Information (OSTI), May 2018. http://dx.doi.org/10.2172/1438157.

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Mosher, Scott W., Aaron M. Bevill, Seth R. Johnson, Ahmad M. Ibrahim, Charles R. Daily, Thomas M. Evans, John C. Wagner, and Jeffrey O. Johnson. ADVANTG An Automated Variance Reduction Parameter Generator. Office of Scientific and Technical Information (OSTI), November 2013. http://dx.doi.org/10.2172/1105937.

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Lewis, Peter A., Richard L. Ressler, and R. K. Wood. Variance Reduction Using Nonlinear Control and Transformations. Fort Belvoir, VA: Defense Technical Information Center, August 1988. http://dx.doi.org/10.21236/ada200471.

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Wakeford, Daniel Tyler. Importance Sampling Variance Reduction in GRESS ATMOSIM. Office of Scientific and Technical Information (OSTI), April 2017. http://dx.doi.org/10.2172/1356092.

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Spencer, Joshua Bradly, Roger Lee Martz, and Jennifer Louise Alwin. Weight Window Based Variance Reduction Introduction & Overview. Office of Scientific and Technical Information (OSTI), May 2018. http://dx.doi.org/10.2172/1438163.

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Mosher, Scott W., Seth R. Johnson, Aaron M. Bevill, Ahmad M. Ibrahim, Charles R. Daily, Thomas M. Evans, John C. Wagner, Jeffrey O. Johnson, and Robert E. Grove. ADVANTG An Automated Variance Reduction Parameter Generator, Rev. 1. Office of Scientific and Technical Information (OSTI), August 2015. http://dx.doi.org/10.2172/1210162.

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Kelly, Maeve Emma. Comparison of MCNP Variance Reduction Techniques for Linear Accelerators. Office of Scientific and Technical Information (OSTI), August 2019. http://dx.doi.org/10.2172/1558942.

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Booth, T. E. Monte Carlo variance reduction approaches for non-Boltzmann tallies. Office of Scientific and Technical Information (OSTI), December 1992. http://dx.doi.org/10.2172/10115861.

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Cramer, S. N., and J. S. Tang. Variance reduction methods applied to deep-penetration Monte Carlo problems. Office of Scientific and Technical Information (OSTI), January 1986. http://dx.doi.org/10.2172/5970446.

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