eprintid: 4581 rev_number: 21 eprint_status: archive userid: 351 dir: disk0/00/00/45/81 datestamp: 2016-01-15 02:06:25 lastmod: 2021-08-27 17:15:23 status_changed: 2016-01-15 02:06:25 type: monograph metadata_visibility: show item_issues_count: 2 creators_name: Varis, O. creators_id: AL1331 title: A Belief Network Approach to Optimization and Parameter Estimation in Resource and Environmental Management Models ispublished: pub internal_subjects: iis_ecl internal_subjects: iis_env internal_subjects: iis_mod internal_subjects: iis_wat abstract: This study presents an approach to use Bayesian belief networks in various optimization tasks in resource and environmental management. A belief network is constructed to work parallel to a deterministic model, and it is used to update conditional probabilities associated with different components of the model. The propagation of probabilistic information occurs in two directions in the network. The divergence between prior and posterior probability distributions at model components can be used as indication on inconsistency between model structure, parameter values, and other information used. An iteration scheme was developed to force prior and posterior distributions to become equal. This removes inconsistencies between different sources of information. The scheme can be used in different optimization tasks including parameter estimation and optimization between various management alternatives. Also multiobjective optimization is possible. The approach is illustrated with two numerical examples and with a hypothetical example on cost-effective management of river water quality. date: 1995-02 date_type: published publisher: WP-95-011 iiasapubid: WP-95-011 price: 10 creators_browse_id: 2475 full_text_status: public monograph_type: working_paper place_of_pub: IIASA, Laxenburg, Austria pages: 51 coversheets_dirty: FALSE fp7_type: info:eu-repo/semantics/book citation: Varis, O. (1995). A Belief Network Approach to Optimization and Parameter Estimation in Resource and Environmental Management Models. IIASA Working Paper. IIASA, Laxenburg, Austria: WP-95-011 document_url: https://pure.iiasa.ac.at/id/eprint/4581/1/WP-95-011.pdf