scholarly journals An agent-based Bayesian forecasting model for enhanced network security

2002 ◽  
Author(s):  
J. Pikoulas ◽  
W.J. Buchanan ◽  
M. Mannion ◽  
K. Triantafyllopoulos
Keyword(s):  
Network Security ◽  
Forecasting Model ◽  
Bayesian Forecasting ◽  
Agent Based

Bayesian Forcasting Model – 2020 Presidential Elections in the United States of America

2021 ◽  
Vol 704 (1) ◽  
pp. 91-104
Author(s):  
Maria Raczyńska
Keyword(s):  
United States ◽  
United States Of America ◽  
Presidential Elections ◽  
Bayesian Models ◽  
The United States ◽  
Point Of View ◽  
Forecasting Model ◽  
Bayesian Forecasting ◽  
Technical Point ◽  
Made In

The article describes and explains a prior centric Bayesian forecasting model for the 2020 US elections.The model is based on the The Economist forecasting project, but strongly differs from it. From the technical point of view, it uses R and Stan programming and Stan software. The article’s focus is on theoretical decisions made in the process of constructing the model and outcomes. It describes why Bayesian models are used and how they are used to predict US presidential elections.

scholarly journals A Bayesian forecasting model of international conflict

2019 ◽  
Vol 17 (3) ◽  
pp. 235-242
Author(s):  
George W Williford ◽  
Douglas B Atkinson
Keyword(s):  
International Relations ◽  
Bayesian Methods ◽  
International Conflict ◽  
Rare Events ◽  
Forecasting Model ◽  
Bayesian Forecasting ◽  
Strong Interest ◽  
Out Of Sample ◽  
Cross National

Scholars and practitioners in international relations have a strong interest in forecasting international conflict. However, due to the complexity of forecasting rare events, existing attempts to predict the onset of international conflict in a cross-national setting have generally had low rates of success. In this paper, we apply Bayesian methods to develop a forecasting model designed to predict the onset of international conflict at the yearly level. We find that this model performs substantially better at producing accurate predictions both in and out of sample.

scholarly journals Multi-Agent based MapReduce Model for Efficient Utilization of System Resources

2018 ◽  
Vol 11 (2) ◽  
pp. 504 ◽  
Author(s):  
Heena Kousar ◽  
B.R. Prasad Babu
Keyword(s):  
Social Media ◽  
Real Time ◽  
Historical Data ◽  
Sensor Data ◽  
Forecasting Model ◽  
Agent Based ◽  
Online Processing ◽  
Inflow Forecasting ◽  
System Resource ◽  
State Of Art

<p>Recently with increased adoption of big data, Internet of Things and sensor technology by various organization for provisioning smart intelligent services for various application uses. Data processing on real-time social media and sensor data is been a key area of research in recent times and these data are massive and continuous. Smart application using sensor and social media data can be classified into three class: 1) online processing of streaming data; 2) online processing of historical data; and 3) hybrid processing of both. The existing model are designed considering stream or batch processing. For provisioning real-time processing MapReduce framework using Hadoop framework is considered by state-of-art technique for data inflow forecasting. However, the Hadoop based forecasting model are not efficient in fully utilizing system resource. Agent based MapReduce forecasting model is adopted by state-of-art technique to utilize system efficiently. However, they incurs high computation overhead, thus increase cost of computing cost. To overcome this work present an agent based Data Inflow Forecasting (DIF) model for both stream and non-stream (historical) data by using Multivariate Gaussian Mixture (MGM) model. This work present an Agent based MapReduce (AMR) framework to process data in real-time and utilize system resource efficiently. To provide scalability for processing social media and sensor data DIF-AMR model adopts cloud computing architecture. Experiment are conducted to evaluate performance of DIF-AMR of over existing model shows significant performance improvement in terms of computation time.</p>

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