Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Probabilistic Earned Value Management Using Bayesian Networks
Date
2018-11-06
Author
Yet, Barbaros
Metadata
Show full item record
Item Usage Stats
61
views
0
downloads
Cite This
URI
https://hdl.handle.net/11511/71773
Collections
Unverified, Conference / Seminar
Suggestions
OpenMETU
Core
Probabilistic aggregation strategies in swarm robotic systems
Soysal, O; Şahin, Erol (2005-06-10)
In this study, a systematic analysis of probabilistic aggregation strategies in swarm robotic systems is presented. A generic aggregation behavior is proposed as a combination of four basic behaviors: obstacle avoidance, approach, repel, and wait. The latter three basic behaviors are combined using a three-state finite state machine with two probabilistic transitions among them. Two different metrics were used to compare performance of strategies. Through systematic experiments, how the aggregation performa...
Probabilistic matrix factorization based collaborative filtering with implicit trust derived from review ratings information
Ercan, Eda; Taşkaya Temizel, Tuğba; Department of Information Systems (2010)
Recommender systems aim to suggest relevant items that are likely to be of interest to the users using a variety of information resources such as user profiles, trust information and users past predictions. However, typical recommender systems suffer from poor scalability, generating incomprehensible and not useful recommendations and data sparsity problem. In this work, we have proposed a probabilistic matrix factorization based local trust boosted recommendation system which handles data sparsity, scalabil...
Probabilistic Life Cycle Cost Analysis Incorporating Multi Attribute Utility Assessment
Birgönül, Mustafa Talat; Dikmen Toker, İrem (1996-01-01)
It has been observed quite frequently that social housing projects addressed for the low and middle income groups have been managed with the achievement of low initial cost only, without given any consideration to user satisfaction and life cycle cost analysis of buildings. As a response to this insufficiency, life cycle cost concept has been introduced at which all the costs of acquisition, operation, maintenance and modification of a building or a facility are taken into account, for the purpose of making...
Probabilistic Wind Power Forecasting by Using Quantile Regression Analysis
Ozkan, Mehmet Baris; Guvengir, Umut; Kucuk, Dilek; Secen, Ali Unver; Buhan, Serkan; Demirci, Turhan; Bestil, Abdullah; Er, Ceyda; Karagöz, Pınar (2017-09-22)
Effective use of renewable energy sources, and in particular wind energy, is of paramount importance. Compared to other renewable energy sources, wind is so fluctuating that it must be integrated to the electricity grid in a planned way. Wind power forecast methods have an important role in this integration. These methods can be broadly classified as point wind power forecasting or probabilistic wind power forecasting methods. The point forecasting methods are more deterministic and they are concerned with ...
Probabilistic distance clustering adjusted for cluster size
BEN-ISRAEL, Adi; İyigün, Cem (Cambridge University Press (CUP), 2008-01-01)
The probabilistic distance clustering method of [1] works well if the cluster sizes are approximately equal. We modify that method to deal with clusters of arbitrary size and for problems where the cluster sizes are themselves unknowns that need to be estimated. In the latter case, our method is a viable alternative to the expectation-maximization (EM) method.
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
B. Yet, “Probabilistic Earned Value Management Using Bayesian Networks,” 2018, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/71773.