+ Site Statistics
+ Search Articles
+ Subscribe to Site Feeds
EurekaMag Most Shared ContentMost Shared
EurekaMag PDF Full Text ContentPDF Full Text
+ PDF Full Text
Request PDF Full TextRequest PDF Full Text
+ Follow Us
Follow on FacebookFollow on Facebook
Follow on TwitterFollow on Twitter
Follow on Google+Follow on Google+
Follow on LinkedInFollow on LinkedIn

+ Translate

The use of conditional probability functions in range data analysis and simulation

Journal of range management 46(2): 157-160
The use of conditional probability functions in range data analysis and simulation
Managers and range scientists are interested in the response of such variables as forage production and animal performance to various environmental and management factors. Due to the inability to control many of the factors affecting range systems, production responses should include distributional information in addition to their expected values. Recent developments in the estimation of conditional probability distribution functions provide the range scientist with a practical procedure to more fully characterize variable responses. The conditional probability distribution approach is applied to an analysis of forage production data from the literature. An illustration of the procedure in range decision analysis derives distributional information on animal performance and net return under several different steer stocking levels.

Accession: 002530244

DOI: 10.2307/4002274

Download PDF Full Text: The use of conditional probability functions in range data analysis and simulation

Related references

Bivariate categorical data analysis using normal linear conditional multinomial probability model. Statistics in Medicine 34(3): 469-486, 2015

Conditional relative survival of cancer patients and conditional probability of death: a French National Database analysis. Cancer 115(19): 4616-4624, 2009

Landslide Susceptibility Mapping Using Remotely Sensed Data through Conditional Probability Analysis Using Seed Cell and Point Sampling Techniques. Journal of the Indian Society of Remote Sensing 40(4): 669-678, 2012

Quantifying risk using probability kriging and conditional simulation. Agronomy Abstracts 85: 304, 1993

Probability density functions in the analysis of hydraulic conductivity data. Journal of Hydrologic Engineering 11(5): 442-450, 2006

Conditional simulation of hydrofacies architecture; a transition probability/ Markov approach. SEPM Concepts in Hydrogeology and Environmental Geology 1(Pages 147-170, 1998

Probability of plume interception using conditional simulation of hydraulic head and inverse modeling. Mathematical Geology 23(2): 219-239, 1991

Comparative performance of indicator algorithms for modeling conditional probability distribution functions. Mathematical Geology 26(3): 389-411, 1994

Conditional simulation with data subject to measurement error; post-simulation filtering with modified factorial kriging. Mathematical Geology 27(6): 749-762, 1995

Entropy statistical spectral conditional probability and determined characteristics of cardiac rhythm in various human functions. Uspekhi Fiziologicheskikh Nauk 19(1): 40-55, 1988