|
ABSTRACT
A rejection algorithm that uses a new method for constructing simple hat functions for a unimodal, bounded density f is introduced called “transformed density rejection.” It is based on the idea of transforming f with a suitable transformation T such that T(f(x)) is concave. f is then called T-concave, and tangents of T(f(x)) in the mode and in a point on the left and right side are used to construct a hat function with a table-mountain shape. It is possible to give conditions for the optimal choice of these points of contact. With T= -1/xxx, the method can be used to construct a universal algorithm that is applicable to a large class of unimodal distributions, including the normal, beta, gamma, and t-distribution.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
 |
1
|
|
| |
2
|
AHRENS, J. H. ANn KOHRT, K.D. 1981. Computer methods for sampling from largely arbitrary statistical distributions. Computing 26, 19-31.
|
| |
3
|
ATKINSON, A.C. 1982. The simulation of generalized inverse gaussian and hyperbolic random variables. SIAM J. Sci. Star. Comput. 3, 502-515.
|
| |
4
|
CHENG, R. C.H. 1977. The generation of gamma variables with non-integral shape parameter. Appl. Stat. 26, 71-75.
|
 |
5
|
|
| |
6
|
|
| |
7
|
DEVROYE, L. 1986. Non-Uniform Random Variate Generation. Springer-Verlag, New York.
|
 |
8
|
|
| |
9
|
GILKS, W. R. AND WILD, P. 1992. Adaptive rejection sampling for gibbs sampling. Appl. Stat. 41,337-348.
|
| |
10
|
HSRMANN, W. 1994. A universal generator for discrete log-concave distributions. Computing 52, 89-96.
|
| |
11
|
H~RMANN, W. AND DERFLINGER, G. 1990. The ACR method for generating normal random variables. OR Spektrum 12, 181-185.
|
| |
12
|
JOHNSON, N. L. AND KOTZ, S. 1970. Continuous Univariate Distributwns. Vol. 1. John Wiley, New York.
|
| |
13
|
JORGENSEN, B. 1982. Statistical Properties of the Generalized Inverse Gaussian Distribution. Lecture Notes in Statistics, vol. 9. Springer, Berlin.
|
| |
14
|
KACHITVlCHYANUKUL, V. AND SCHMEISER, B.W. 1985. Computer generation of hypergeometric random variates. J. Stat. Comput. Simul. 22, 127-145.
|
 |
15
|
|
| |
16
|
KINDERMAN, A. J. AND MONAHAN, F. J. 1980. New methods for generating student's t and gamma variables. Computing 25, 369-377.
|
| |
17
|
MARSAGLIA, G. AND TSANG, W.W. 1984. A fast, easily implemented method for sampling from decreasing or symmetric unimodal density functions. SIAM J. Sci. Star. Comput. 5, 349-359.
|
| |
18
|
SCHMEISER, B. W. AND BABU, A. J.G. 1980. Beta variate generation via exponential majorizing functions. Oper. Res. 28, 917-926.
|
| |
19
|
SCHMEISER, B. W. AND LAL, R. 1980. Squeeze methods for generating gamma variates. J. Am. Stat. Assoc. 75, 679-682.
|
| |
20
|
|
| |
21
|
WAKEFIELD, J. C., GELFAND, A. E., AND SMITH, A. F.M. 1991. Efficient generation of random variates via the ratio-of-uniforms method. Stat. Comput. 1, 129-133.
|
| |
22
|
ZECHNER, H. AND STADLOBER, E. 1993. Generating beta variates via patchwork rejection. Computing 50, 1-18.
|
Peer to Peer - Readers of this Article have also read:
-
Data structures for quadtree approximation and compression
Communications of the ACM
28, 9
Hanan Samet
-
A hierarchical single-key-lock access control using the Chinese remainder theorem
Proceedings of the 1992 ACM/SIGAPP Symposium on Applied computing
Kim S. Lee
, Huizhu Lu
, D. D. Fisher
-
Putting innovation to work: adoption strategies for multimedia communication systems
Communications of the ACM
34, 12
Ellen Francik
, Susan Ehrlich Rudman
, Donna Cooper
, Stephen Levine
-
The GemStone object database management system
Communications of the ACM
34, 10
Paul Butterworth
, Allen Otis
, Jacob Stein
-
An intelligent component database for behavioral synthesis
Proceedings of the 27th ACM/IEEE Design Automation Conference on
Gwo-Dong Chen
, Daniel D. Gajski
|