Access

You are not currently logged in.

Access your personal account or get JSTOR access through your library or other institution:

login

Log in to your personal account or through your institution.

A Unified View of the IPA, SF, and LR Gradient Estimation Techniques

Pierre L'Ecuyer
Management Science
Vol. 36, No. 11 (Nov., 1990), pp. 1364-1383
Published by: INFORMS
Stable URL: http://www.jstor.org/stable/2632612
Page Count: 20
  • Download ($30.00)
  • Cite this Item
A Unified View of the IPA, SF, and LR Gradient Estimation Techniques
Preview not available

Abstract

We study the links between the likelihood-ratio (LR) gradient-estimation technique (sometimes called the score-function (SF) method), and infinitesimal perturbation analysis (IPA). We show how IPA can be viewed as a (degenerate) special case of the LR and SF techniques by selecting an appropriate representation of the underlying sample space for a given simulation experiment. We also show how different definitions of the sample space yield different variants of the LR method, some of them mixing IPA with more straightforward LR. We illustrate this by many examples. We also give sufficient conditions under which the gradient estimators are unbiased.

Page Thumbnails

  • Thumbnail: Page 
1364
    1364
  • Thumbnail: Page 
1365
    1365
  • Thumbnail: Page 
1366
    1366
  • Thumbnail: Page 
1367
    1367
  • Thumbnail: Page 
1368
    1368
  • Thumbnail: Page 
1369
    1369
  • Thumbnail: Page 
1370
    1370
  • Thumbnail: Page 
1371
    1371
  • Thumbnail: Page 
1372
    1372
  • Thumbnail: Page 
1373
    1373
  • Thumbnail: Page 
1374
    1374
  • Thumbnail: Page 
1375
    1375
  • Thumbnail: Page 
1376
    1376
  • Thumbnail: Page 
1377
    1377
  • Thumbnail: Page 
1378
    1378
  • Thumbnail: Page 
1379
    1379
  • Thumbnail: Page 
1380
    1380
  • Thumbnail: Page 
1381
    1381
  • Thumbnail: Page 
1382
    1382
  • Thumbnail: Page 
1383
    1383