Grouping Edges: An Efficient Bayesian Multiple Hypothesis Approach
Research output: Contribution to journal › Journal article › Research › peer-review
Standard
Grouping Edges: An Efficient Bayesian Multiple Hypothesis Approach. / Cox, INGEMAR J; Rehg, JAMES M; Hingorani, SUNITA L; Miller, MATT L.
In: Partitioning Data Sets, 1995, p. 199-235.Research output: Contribution to journal › Journal article › Research › peer-review
Harvard
Cox, INGEMARJ, Rehg, JAMESM, Hingorani, SUNITAL & Miller, MATTL 1995, 'Grouping Edges: An Efficient Bayesian Multiple Hypothesis Approach', Partitioning Data Sets, pp. 199-235.
APA
Cox, INGEMAR. J., Rehg, JAMES. M., Hingorani, SUNITA. L., & Miller, MATT. L. (1995). Grouping Edges: An Efficient Bayesian Multiple Hypothesis Approach. Partitioning Data Sets, 199-235.
Vancouver
Cox INGEMARJ, Rehg JAMESM, Hingorani SUNITAL, Miller MATTL. Grouping Edges: An Efficient Bayesian Multiple Hypothesis Approach. Partitioning Data Sets. 1995;199-235.
Author
Bibtex
@article{a52e4dfccf9443fab5a8c3d8510270a9,
title = "Grouping Edges: An Efficient Bayesian Multiple Hypothesis Approach",
author = "Cox, {INGEMAR J} and Rehg, {JAMES M} and Hingorani, {SUNITA L} and Miller, {MATT L}",
year = "1995",
language = "English",
pages = "199--235",
journal = "Partitioning Data Sets",
}
RIS
TY - JOUR
T1 - Grouping Edges: An Efficient Bayesian Multiple Hypothesis Approach
AU - Cox, INGEMAR J
AU - Rehg, JAMES M
AU - Hingorani, SUNITA L
AU - Miller, MATT L
PY - 1995
Y1 - 1995
M3 - Journal article
SP - 199
EP - 235
JO - Partitioning Data Sets
JF - Partitioning Data Sets
ER -
ID: 98294919