• DocumentCode
    3596962
  • Title

    Bayesian statistics and the Monte Carlo method

  • Author

    Herzog, Thomas N.

  • Author_Institution
    Office of Evaluation, U.S. Dept. of Housing & Urban Dev., Washington, DC, USA
  • Volume
    1
  • fYear
    2002
  • Firstpage
    136
  • Abstract
    We discuss the application of the Bayesian statistical paradigm in conjunction with Monte Carlo methods to practical problems. We begin by describing the basic constructs of the Bayesian paradigm. We then discuss two applications. The first entails the simulation of a two-stage model of a property-casualty insurance operation. The second application simulates the operation of an insurance regime for home equity conversion mortgages (also known as reverse mortgages). In this simulation, we built separate models to (1) predict the appreciation of individual home values and (2) predict the annual mortality experience of individual insureds. A feature of this work was the simulation of the parameters of these models in order to explicitly incorporate their variability into the model. We conclude the work by considering (1) model validation issues and (2) alternate forms of scenario testing - i.e., those employing pseudorandom numbers, quasi-random numbers, or even more subjective schemes.
  • Keywords
    Bayes methods; Monte Carlo methods; digital simulation; insurance data processing; Bayesian statistical paradigm; Monte Carlo methods; annual mortality experience; home equity conversion mortgages; model validation issues; property-casualty insurance operation; pseudorandom numbers; quasi-random numbers; reverse mortgages; scenario testing; subjective schemes; two-stage model; Bayesian methods; Density functional theory; Frequency; Insurance; Loans and mortgages; Predictive models; Probability; Random variables; Statistics; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Simulation Conference, 2002. Proceedings of the Winter
  • Print_ISBN
    0-7803-7614-5
  • Type

    conf

  • DOI
    10.1109/WSC.2002.1172877
  • Filename
    1172877