IDEAS home Printed from https://ideas.repec.org/a/spr/coopap/v51y2012i1p305-321.html
   My bibliography  Save this article

Monte Carlo algorithm for trajectory optimization based on Markovian readings

Author

Listed:
  • Ronaldo Dias
  • Nancy Garcia
  • Adriano Zambom

Abstract

No abstract is available for this item.

Suggested Citation

  • Ronaldo Dias & Nancy Garcia & Adriano Zambom, 2012. "Monte Carlo algorithm for trajectory optimization based on Markovian readings," Computational Optimization and Applications, Springer, vol. 51(1), pages 305-321, January.
  • Handle: RePEc:spr:coopap:v:51:y:2012:i:1:p:305-321
    DOI: 10.1007/s10589-010-9337-3
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10589-010-9337-3
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s10589-010-9337-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Harvey,Andrew C., 1991. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521405737, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Adriano Zanin Zambom & Brian Seguin & Feifei Zhao, 2019. "Robot path planning in a dynamic environment with stochastic measurements," Journal of Global Optimization, Springer, vol. 73(2), pages 389-410, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Avanzi, Benjamin & Taylor, Greg & Vu, Phuong Anh & Wong, Bernard, 2020. "A multivariate evolutionary generalised linear model framework with adaptive estimation for claims reserving," Insurance: Mathematics and Economics, Elsevier, vol. 93(C), pages 50-71.
    2. Prilly Oktoviany & Robert Knobloch & Ralf Korn, 2021. "A machine learning-based price state prediction model for agricultural commodities using external factors," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 1063-1085, December.
    3. David Bolder & Shudan Liu, 2007. "Examining Simple Joint Macroeconomic and Term-Structure Models: A Practitioner's Perspective," Staff Working Papers 07-49, Bank of Canada.
    4. Yuo-Hsien Shiau & Su-Fen Yang & Rishan Adha & Syamsiyatul Muzayyanah, 2022. "Modeling Industrial Energy Demand in Relation to Subsector Manufacturing Output and Climate Change: Artificial Neural Network Insights," Sustainability, MDPI, vol. 14(5), pages 1-18, March.
    5. Clements, Kenneth W. & Fry, Renée, 2008. "Commodity currencies and currency commodities," Resources Policy, Elsevier, vol. 33(2), pages 55-73, June.
    6. Faust, Jon & Gupta, Abhishek, 2010. "Posterior Predictive Analysis for Evaluating DSGE Models," MPRA Paper 26721, University Library of Munich, Germany.
    7. Rutger-Jan Lange & Coen N. Teulings, 2021. "The option value of vacant land: Don't build when demand for housing is booming," Tinbergen Institute Discussion Papers 21-022/IV, Tinbergen Institute.
    8. Matteo Barigozzi & Matteo Luciani, 2019. "Quasi Maximum Likelihood Estimation and Inference of Large Approximate Dynamic Factor Models via the EM algorithm," Papers 1910.03821, arXiv.org, revised Sep 2024.
    9. Zirogiannis, Nikolaos & Tripodis, Yorghos, 2013. "A Generalized Dynamic Factor Model for Panel Data: Estimation with a Two-Cycle Conditional Expectation-Maximization Algorithm," Working Paper Series 142752, University of Massachusetts, Amherst, Department of Resource Economics.
    10. Tobias Hartl & Roland Jucknewitz, 2022. "Approximate state space modelling of unobserved fractional components," Econometric Reviews, Taylor & Francis Journals, vol. 41(1), pages 75-98, January.
    11. Moshe Buchinsky & Phillip Leslie, 2010. "Educational Attainment and the Changing U.S. Wage Structure: Dynamic Implications on Young Individuals' Choices," Journal of Labor Economics, University of Chicago Press, vol. 28(3), pages 541-594, July.
    12. S. Boragan Aruoba & Francis X. Diebold, 2010. "Real-Time Macroeconomic Monitoring: Real Activity, Inflation, and Interactions," American Economic Review, American Economic Association, vol. 100(2), pages 20-24, May.
    13. Ippei Fujiwara & Koji Takahashi, 2012. "Asian Financial Linkage: Macro‐Finance Dissonance," Pacific Economic Review, Wiley Blackwell, vol. 17(1), pages 136-159, February.
    14. Hongsheng Bi & Rubao Ji & Hui Liu & Young-Heon Jo & Jonathan A Hare, 2014. "Decadal Changes in Zooplankton of the Northeast U.S. Continental Shelf," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-12, January.
    15. Reema Gh. Alajmi, 2024. "Energy Consumption and Carbon Emissions: An Empirical Study of Saudi Arabia," Sustainability, MDPI, vol. 16(13), pages 1-16, June.
    16. Leif Anders Thorsrud, 2016. "Nowcasting using news topics. Big Data versus big bank," Working Paper 2016/20, Norges Bank.
    17. Knut Are Aastveit & Karsten R. Gerdrup & Anne Sofie Jore & Leif Anders Thorsrud, 2014. "Nowcasting GDP in Real Time: A Density Combination Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(1), pages 48-68, January.
    18. Obryan Poyser, 2017. "Exploring the determinants of Bitcoin's price: an application of Bayesian Structural Time Series," Papers 1706.01437, arXiv.org.
    19. Škare, Marinko & Mošnja-Škare, Lorena, 2019. "Economic policy implications of the Gibson Law in the Netherlands (1800–2012)," Journal of Policy Modeling, Elsevier, vol. 41(5), pages 926-942.
    20. Rob Luginbuhl, 2020. "Estimation of the Financial Cycle with a Rank-Reduced Multivariate State-Space Model," CPB Discussion Paper 409, CPB Netherlands Bureau for Economic Policy Analysis.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:coopap:v:51:y:2012:i:1:p:305-321. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.