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Stochastic analysis and finance and insurance and risk,
Statistics
Publications

Dahl, Kristina Rognlien & Huseby, Arne (2018). Buffered environmental contours, In Stein Haugen; Anne Barros; Coen van Gulijk; Trond Kongsvik & Jan Erik Vinnem (ed.),
Safety and Reliability  Safe Societies in a Changing World.
Taylor & Francis.
ISBN 9781351174664.
281.
s 2285
 2292
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The main idea of this paper is to use the notion of buffered failure probability from probabilistic structural design, first introduced by Rockafellar and Royset (2010), to introduce buffered environmental contours. Classical environmental contours are used in structural design in order to obtain upper bounds on the failure probabilities of a large class of designs. The purpose of buffered failure probabilities is the same. However, in contrast to classical environmental contours, this new concept does not just take into account failure vs. functioning, but also to which extent the system is failing. For example, this is relevant when considering the risk of flooding: We are not just interested in knowing whether a river has flooded. The damages caused by the flooding greatly depends on how much the water has risen above the standard level.

Huseby, Arne & Rabbe, Marit (2018). Optimizing warnings for slippery runways based on weather data, In Stein Haugen; Anne Barros; Coen van Gulijk; Trond Kongsvik & Jan Erik Vinnem (ed.),
Safety and Reliability  Safe Societies in a Changing World.
Taylor & Francis.
ISBN 9781351174664.
280.
s 2789
 2796
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Slippery runways represent a significant risk to aircrafts especially during the winter season. In order to apply the appropriate braking action, the pilots need reliable information about the runway conditions. Unfortunately the accuracy of runway reports can sometimes be unsatisfactory. In order to obtain more precise and uptodate information about the current conditions, a warning system based on various types of weather data was suggested by Huseby and Rabbe (2012). Huseby and Rabbe (2008) and Huseby et al. (2010). The system is based on a set of scenarios known to cause slippery conditions. By monitoring meteorological parameters like air and ground temperature, humidity, visibility and precipitation, and comparing these to the given scenarios, the system can issue warnings to the ground personnel. This system is currently being used on 16 Norwegian airports. In the present paper this warning system is reviewed. Ideally, the warning system should issue warnings whenever the estimated runway conditions are medium or worse. At the same time the system should not issue warnings when the runway conditions are good. Thus, there are two types of errors we need to take into consideration. Type 1 errors occur when the system does not issue a warning even though the conditions are medium or worse, while Type 2 errors occur if a warning is issued when the conditions are good. When designing the system, we need to find the optimal balance between these types of errors taking into account that a Type 1 error to a certain degree is considered to be worse than a Type 2 error. The paper describes how the system can be optimized using a combination of weather data and flight data.

Vanem, Erik & Huseby, Arne (2018). Combined LongTerm and ShortTerm Description of Extreme Ocean Wave Conditions by 3Dimensional Environmental Contours, In
The Proceedings of The Twentyeighth (2018) International OCEAN AND POLAR ENGINEERING CONFERENCE, ISOPE 2018.
International Society of Ocean and Polar Engineers (ISOPE).
ISBN 9781880653876.
paper.
s 470
 477

Huseby, Arne (2017). Optimizing energy production systems under uncertainty, In Lesley Walls; Matthew Revie & Tim Bedford (ed.),
Risk, Reliability and Safety: Innovating Theory and Practice : Proceedings of ESREL 2016 (Glasgow, Scotland, 2529 September 2016).
CRC Press.
ISBN 9781138029972.
13.6.
s 1619
 1626
Full text in Research Archive.
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Electricity infrastructure has become a critical element of modern industrial society. In order to model and analyse this infrastructure, identify weaknesses, and optimize performance, one needs to take into account its distributed nature. Rather than modelling a single system, energy production and distribution systems consists of many more or less autonomous subsystems working together and trading with each other. Analytical models could perhaps be used to describe a single subsystem. However the complexity related to the interactions between the subsystems soon becomes unmanageable. Even establishing a simulation model for such phenomenons is a nontrivial task, especially if the model is required to be easily scaleable. In this paper we consider the problem of optimizing a simplified energy system with respect to supply stability. This is done using both deterministic methods and Monte Carlo methods. The system is broken into smaller units. These units may trade energy between them in order to maintain a stable supply covering the demand. An important element in the model is the ability to store energy within the unit. For some units, e.g., hydroelectric power plants, the energy can be easily stored in the form of a water reservoir. For other units, like wind power plants, storing energy is usually not feasible. By using an object oriented software framework, we can compare different production units, and study how these can interact in order to facilitate a stable total production.

Huseby, Arne; Vanem, Erik & Eskeland, Karoline (2017). Evaluating properties of environmental contours, In Marko Cepin & Radim Bris (ed.),
Safety & Reliability, Theory and Applications.
CRC Press.
ISBN 9781138629370.
264.
s 2101
 2109
Full text in Research Archive.
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Environmental contours are widely used as a basis for e.g., ship design. The traditional approach to environmental contours is based on the wellknown Rosenblatt transformation. However, due to the effects of this transformation the probabilistic properties of the resulting environmental contour can be difficult to interpret. An alternative approach to environmental contours uses Monte Carlo simulations on the joint environmental model, and thus obtain a contour without the need for the Rosenblatt transformation. This contour have welldefined probabilistic properties, but may sometimes be overly conservative in certain areas. In this paper we give a precise definition of the concept of exceedence probability which is valid for all types of environmental contours. Moreover, we show how to estimate the exceedence probability of a given environmental contour, and use this to compare different approaches to contour construction. The methods are illustrated by numerical examples based on reallife data.

Lindqvist, Bo Henry; Samaniego, Francisco J. & Huseby, Arne (2016). On the equivalence of systems of different sizes, with applications to system comparisons. Advances in Applied Probability.
ISSN 00018678.
48(2), s 332 348 . doi:
10.1017/apr.2016.3

Huseby, Arne & Thomsen, Jan (2015). Quantifying operational risk exposure by combining incident data and subjective risk assessments, In Luca Podofillini; Bruno Sudret; Bozidar Stojadinovic; Enrico Zio & Wolfgang Kröder (ed.),
Safety and Reliability of Complex Engineered Systems.
CRC Press.
ISBN 9781138028791.
57.
s 443
 451
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Quantifying operational risk exposure typically involves gathering information from several sources, including historical data as well as subjective assessments. Using historical data one can estimate both an incident frequency distribution, as well as an incident consequence distribution. Based on these two distributions a simulation model can be established. However, by limiting the focus to data related to incidents which may reappear in the future, one is often left with a relatively short incident history. In order to improve the risk quantification, it is often necessary to include subjective risk assessments as well. In the present paper we propose three models for how to combine these two sources of information. In the first model we assume that the two sources are completely disjoint, while in the second model the two sources are assumed to overlap completely. The third model represents an intermediate situation where the two sources are partially overlapping. This third model contains the two first models as limiting cases. The models are illustrated and compared in an extensive numerical example.
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Skutlaberg, Kristina; Huseby, Arne & Natvig, Bent (2018). Partial monitoring of multistate systems. Statistical research report (Universitetet i Oslo. Matematisk institut. 1.
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For large multicomponent systems it is typically too costly to monitor the entire system constantly. In the present paper we consider a case where a component is unobserved in a time interval [0, T]. Here T is a stochastic variable with a distribution which depends om the structure of the system and the lifetime distribution of the other components. Thus, different systems will result in different distributions of T, the main focus of the paper is on how the unobserved period of time affects what we learn about the unobserved component during this period. We analyse this by considering three different cases. In the first case we consider both T as well as the state of the unobserved component at time T as given. In the second case we allow the state of the unobserved component at time T to be stochastic, while in the third case both T and the state are treated as stochastic variable. In all cases we study the problem using preposterior analysis. That is, we investigate how much information we can expect to get by the end of the time interval [0, T]. The methodology is also illustrated on a more complete example.

Huseby, Arne (2017). On orientable matroid systems and reliability equivalence. Statistical research report (Universitetet i Oslo. Matematisk institut. 1. Full text in Research Archive.

Lilleborge, Marie; Hauge, Ragnar; Eidsvik, Jo & Huseby, Arne (2016). Efficient Information Gathering in Discrete Bayesian Networks. Series of dissertations submitted to the Faculty of Mathematics and Natural Sciences, University of Oslo.. No. 1796. Full text in Research Archive.
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Effektiv informasjonsinnhenting i Bayesianske Nettverk
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Published Nov. 30, 2010 11:20 PM
 Last modified Oct. 22, 2014 10:47 PM