537 resultados para Statistical decision


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This study determined factors which influenced Canadian provincial (state) politicians when making funding decisions for public libraries. Using the case study methodology, Canadian provincial/state-level funding for public libraries in the 2009-2010 fiscal year was examined. The data were analyzed to determine whether Cialdini’s theory of influence and specifically any of the six tactics of influence (i.e., commitment and consistency, authority, liking, social proof, scarcity, and reciprocity) were instrumental in these budgetary decision-making processes. Findings show the principles of “authority,” “consistency and commitment,” and “liking” were relevant, and that “liking” was especially important to these decisions.

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A probabilistic method is proposed to evaluate voltage quality of grid-connected photovoltaic (PV) power systems. The random behavior of solar irradiation is described in statistical terms and the resulting voltage fluctuation probability distribution is then derived. Reactive power capabilities of the PV generators are then analyzed and their operation under constant power factor mode is examined. By utilizing the reactive power capability of the PV-generators to the full, it is shown that network voltage quality can be greatly enhanced.

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Bayesian experimental design is a fast growing area of research with many real-world applications. As computational power has increased over the years, so has the development of simulation-based design methods, which involve a number of algorithms, such as Markov chain Monte Carlo, sequential Monte Carlo and approximate Bayes methods, facilitating more complex design problems to be solved. The Bayesian framework provides a unified approach for incorporating prior information and/or uncertainties regarding the statistical model with a utility function which describes the experimental aims. In this paper, we provide a general overview on the concepts involved in Bayesian experimental design, and focus on describing some of the more commonly used Bayesian utility functions and methods for their estimation, as well as a number of algorithms that are used to search over the design space to find the Bayesian optimal design. We also discuss other computational strategies for further research in Bayesian optimal design.

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Purpose – Rapid urbanisation, fragmented governance and recurrent flooding complicates resolution of DKI Jakarta’s chronic housing shortage. Failure to effectively implement planning decisionmaking processes poses potential human rights violations. Contemporary planning policy requires the relocation of households living in floodplains within fifteen metres of DKI Jakarta’s main watercourses; further constraining land availability and potentially requiring increased densification. The purpose of this paper is to re-frame planning decision-making to address risks of flooding and to increase community resilience. Design/methodology/approach – This paper presents a preliminary scoping study for a technologically enhanced participatory planning method, incorporating synthesis of existing information on urbanisation, governance, and flood risk management in Jakarta. Findings – Responsibility for flood risk management in DKI Jakarta is fragmented both within and across administrative boundaries. Decision-making is further complicated by: limited availability of land use data; uncertainty as to the delineated extent of watercourses, floodplains, and flood modelling; unclear risk and liability for infrastructure investments; and technical literacy of both public and government participants. Practical implications – This research provides information to facilitate consultation with government entities tasked with re-framing planning processes to increase public participation. Social implications – Reduction in risk exposure amongst DKI Jakarta’s most vulnerable populations addresses issues of social justice.

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Road asset managers are seeking analysis of the whole road network to supplement statistical analyses of small subsets of homogeneous roadway. This study outlines the use of data mining capable of analyzing the wide range of situations found on the network, with a focus on the role of skid resistance in the cause of crashes. Results from the analyses show that on non-crash-prone roads with low crash rates, skid resistance contributes only in a minor way, whereas on high-crash roadways, skid resistance often contributes significantly in the calculation of the crash rate. The results provide evidence supporting a causal relationship between skid resistance and crashes and highlight the importance of the role of skid resistance in decision making in road asset management.

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This thesis deals with the issues of quantifying economic values of coastal and marine ecosystem services and assessing their use in decision-making. The first analytical part of the thesis focuses on estimating non-market use and non-use values, with an application in New-Caledonia using Discrete Choice Experiment. The second part examines how and to what extent the economic valuation of ecosystem services is used in coastal management decision-making with an application in Australia. Using a multi-criteria analysis, the relative importance of ecological, social and economic evaluation criteria is also assessed in the context of coastal development.

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This paper presents a case study for the application of a Linear Engineering Asset Renewal decision support software tool (LinEAR) at a water distribution network in Australia. This case study examines how the LinEAR can assist water utilities to minimise their total pipeline management cost, to make a long-term budget based on mathematically predicted expenditure, and to present calculated evidence for supporting their expenditure requirements. The outcomes from the study on pipeline renewal decision support demonstrate that LinEAR can help water utilities to improve the decision process and save renewal costs over a long-term by providing an optimum renewal schedules. This software can help organisation to accumulate technical knowledge and prediction future impact of the decision using what-if analysis.

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This paper addresses the topic of real-time decision making by autonomous city vehicles. Beginning with an overview of the state of research, the paper presents the vehicle decision making & control systemarchitecture, explains the subcomponents which are relevant for decision making (World Model and Driving Maneuver subsystem), and presents the decision making process. Experimental test results confirmthe suitability of the developed approach to deal with the complex real-world urban traffic.

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This paper addresses the topic of real-time decision making for autonomous city vehicles, i.e. the autonomous vehicles’ ability to make appropriate driving decisions in city road traffic situations. After decomposing the problem into two consecutive decision making stages, and giving a short overview about previous work, the paper explains how Multiple Criteria Decision Making (MCDM) can be used in the process of selecting the most appropriate driving maneuver.

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This thesis addresses the topic of real-time decision making by driverless (autonomous) city vehicles, i.e. their ability to make appropriate driving decisions in non-simplified urban traffic conditions. After addressing the state of research, and explaining the research question, the thesis presents solutions for the subcomponents which are relevant for decision making with respect to information input (World Model), information output (Driving Maneuvers), and the real-time decision making process. TheWorld Model is a software component developed to fulfill the purpose of collecting information from perception and communication subsystems, maintaining an up-to-date view of the vehicle’s environment, and providing the required input information to the Real-Time Decision Making subsystem in a well-defined, and structured way. The real-time decision making process consists of two consecutive stages. While the first decision making stage uses a Petri net to model the safetycritical selection of feasible driving maneuvers, the second stage uses Multiple Criteria Decision Making (MCDM) methods to select the most appropriate driving maneuver, focusing on fulfilling objectives related to efficiency and comfort. The complex task of autonomous driving is subdivided into subtasks, called driving maneuvers, which represent the output (i.e. decision alternatives) of the real-time decision making process. Driving maneuvers are considered as implementations of closed-loop control algorithms, each capable of maneuvering the autonomous vehicle in a specific traffic situation. Experimental tests in both a 3D simulation and real-world experiments attest that the developed approach is suitable to deal with the complexity of real-world urban traffic situations.

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This paper critically examines research on consumer attitudes and behavior towards solar photovoltaic (PV) and renewable energy technology in Australia. The uptake of renewable energy technology by residential consumers in Australia in the past decade has transformed the electricity supply and demand paradigm. Thus, this paper reviews Australian research on consumer behavior, understanding and choices in order to identify gaps in knowledge. As the role of the consumer transforms there is a critical need to understand the ways consumers may respond to future energy policies to mitigate unforeseen negative social and economic consequence of programs designed to achieve positive environmental outcomes.