Browsing by Author "Vanhatalo, Jarno"
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Item Calibrating Expert Assessments Using Hierarchical Gaussian Process Models(INT SOC BAYESIAN ANALYSIS, 2020-12) Perälä, Tommi A.; Vanhatalo, Jarno; Chrysafi, Anna; Department of Built Environment; Water and Environmental Eng.; University of Jyväskylä; University of HelsinkiExpert assessments are routinely used to inform management and other decision making. However, often these assessments contain considerable biases and uncertainties for which reason they should be calibrated if possible. Moreover, coherently combining multiple expert assessments into one estimate poses a long-standing problem in statistics since modeling expert knowledge is often difficult. Here, we present a hierarchical Bayesian model for expert calibration in a task of estimating a continuous univariate parameter. The model allows experts’ biases to vary as a function of the true value of the parameter and according to the expert’s background. We follow the fully Bayesian approach (the so-called supra-Bayesian approach) and model experts’ bias functions explicitly using hierarchical Gaussian processes. We show how to use calibration data to infer the experts’ observation models with the use of bias functions and to calculate the bias corrected posterior distributions for an unknown system parameter of interest. We demonstrate and test our model and methods with simulated data and a real case study on data-limited fisheries stock assessment. The case study results show that experts’ biases vary with respect to the true system parameter value and that the calibration of the expert assessments improves the inference compared to using uncalibrated expert assessments or a vague uniform guess. Moreover, the bias functions in the real case study show important differences between the reliability of alternative experts. The model and methods presented here can be also straightforwardly applied to other applications than our case study.Item A comprehensive approach to scenario-based risk management for Arctic waters(Schiffahrts Verlag Hansa, 2022-09-02) Bergström, Martin; Browne, Thomas; Ehlers, Sören; Helle, Inari; Herrnring, Hauke; Khan, Faisal; Kubiczek, Jan; Kujala, Pentti; Korgesaar, Mihkel; Leira, Bernt Johan; Parviainen, Tuuli; Polojarvi, Arttu; Suominen, Mikko; Taylor, Rocky; Tuhkuri, Jukka; Vanhatalo, Jarno; Veitch, Brian; Department of Mechanical Engineering; Marine Technology; Solid Mechanics; Memorial University of Newfoundland; University of Helsinki; Hamburg University of Technology; Norwegian University of Science and Technology; Tallinn University of TechnologyWhile society benefits from Arctic shipping, it is necessary to recognize that ship operations in Arctic waters pose significant risks to people, the environment, and property. To support the management of those risks, this article presents a comprehensive approach addressing both short-term operational risks, as well as risks related to long-term extreme ice loads. For the management of short-term operational risks, an extended version of the Polar Operational Limit Assessment Risk Indexing System (POLARIS) considering the magnitude of the consequences of potential adverse events is proposed. For the management of risks related to long-term extreme ice loads, guidelines are provided for using existing analytical, numerical, and semi-empirical methods. In addition, to support the design of ice class ship structures, the article proposes a novel approach that can be used in the conceptual design phase for the determination of preliminary scantlings for primary hull structural members.Item Harva Gaussinen prosessi spatiaalisessa epidemiologiassa(Helsinki University of Technology, 2006) Vanhatalo, Jarno; Vehtari, Aki; TkT; Department of Electrical and Communications Engineering; Sähkö- ja tietoliikennetekniikan osasto; Laboratory of Computational Engineering; Laskennallisen tekniikan laboratorio; Lampinen, Jouko; Prof.Tässä diplomityössä esitetään hierarkinen Bayesilainen malli tautikartoituksen avuksi. Tautikartoitus on spatiaalisen epidemiologian osa-alue, jonka tavoitteena on tutkia terveysriskin maantieteellistä vaihtelua. Tavoitteena on kuvata taudin jakautumista kartalla ja korostaa alueita, joissa tauti- tai kuolemanriski ovat kohonneita. Tässä työssä käytetään kolmen hierarkiakerroksen mallia tutkimaan kuolleisuusriskin alueellisia vaihteluja kuolleisuusdatasta. Kuolleisuus tietyllä alueella mallinnetaan Poissonin prosessilla, jonka odotusarvo saadaan vakioidun kuolleisuusriskin ja suhteellisen riskin tulona. Kuolleisuusriski vakioidaan taustapopulaation ikä-, sukupuoli- ja koulutustasojakauman avulla. Suhteellisen riskin logaritmille annetaan prioriksi Gaussinen prosessi, joka tasoittaa riskipintaa ja lisää alueiden väliset korrelaatiot malliin. Gaussisen prosessin ongelmaksi muodostuu kovarianssimatriisin inversioon tarvittava aika, jota pienennetään tekemällä Gaussiselle prosessille harva aproksimaatio. Spatiaalisessa epidemiologiassa on tärkeää pystyä määrittämään tautiriskin alueellisen vaihtelun tilastollinen merkittävyys. Jotta mallin epävarmuusestimaateille saataisiin mahdollisimman hyvät arviot suoritetaan mallin parametrien ylitse integrointi Markov ketju Monte Carlo menetelmiä käyttäen. Gaussisen prosessin latenttien muuttujien näytteistämistä nopeutetaan muunnoksella, joka käyttää hyväkseen posteriorijakauman kovarianssin aproksimaatiota. Markov-ketju-näytteistäminen suoritetaan hybrid Monte Carlo -menetelmällä, jonka oleellinen osa on marginaaliuskottavuuden logaritmin gradienttien laskenta. Harvan aproksimaation tapauksessa gradientit lasketaan muodostamatta eksplisiittisesti täyttä kovarianssimatriisia. Työ esittelee latenttien muuttujien muunnoksen ja gradienttien laskennan toteutukset. Täyttä ja harvaa Gaussista prosessia käyttäviä malleja testataan kahteen kuolemansyydataan neljällä eri kovarianssifunktiolla, ja malleja verrataan keskenään käyttäen DIC-informaatiokriteeriä. Kuolemansyydatan analyysin tulokset esitetään kuolemanriskikarttoina.Item Probability of a ship becoming beset in ice along the Northern Sea Route – A Bayesian analysis of real-life data(Elsevier, 2021-04) Vanhatalo, Jarno; Huuhtanen, Juri; Bergström, Martin; Helle, Inari; Mäkinen, Jussi; Kujala, Pentti; Department of Mechanical Engineering; Marine Technology; University of Helsinki; Department of Mechanical EngineeringShips operating in ice-infested Arctic waters are exposed to a range of ship-ice interaction related hazards. One of the most dangerous of these is the possibility of a ship becoming beset in ice, meaning that a ship is surrounded by ice preventing it from maneuvering under its own power. Such a besetting event may not only result in severe operational disruption, but also expose a ship to severe ice loading or cause it to drift towards shallow water. This may cause significant structural damage to a ship and potentially jeopardize its safety. To support safe and sustainable Arctic shipping operations, this article presents a probabilistic approach to assess the probability of a ship becoming beset in ice. To this end, the proposed approach combines different types of data, including Automatic Identification System (AIS) data, satellite ice data, as well as data on real-life ship besetting events. Based on this data, using a hierarchical Bayesian model, the proposed approach calculates the probability of a besetting event as a function of the Polar Ship Category of a ship, sea area, and the distance travelled in the prevailing ice concentration. The utility of the proposed approach, e.g. in supporting spatiotemporal risk assessments of Arctic shipping activities as well as Arctic voyage planning, is demonstrated through a case study in which the approach is applied to ships operating in the Northern Sea Route (NSR) area. The outcomes of the case study indicate that the probability of besetting is strongly dependent on the Polar Ship Category of a ship and that the probability increases significantly with higher ice concentrations. The sea area, on the other hand, does not appear to significantly affect the probability of besetting.Item Speeding up the inference in Gaussian process models(Aalto-yliopiston teknillinen korkeakoulu, 2010) Vanhatalo, Jarno; Vehtari, Aki, Dr. Tech.; Lääketieteellisen tekniikan ja laskennallisen tieteen laitos; Department of Biomedical Engineering and Computational Science; Aalto-yliopiston teknillinen korkeakoulu; Lampinen, Jouko, Prof.In this dissertation Gaussian processes are used to define prior distributions over latent functions in hierarchical Bayesian models. Gaussian process is a non-parametric model with which one does not need to fix the functional form of the latent function, but its properties can be defined implicitly. These implicit statements are encoded in the mean and covariance function, which determine, for example, the smoothness and variability of the function. This non-parametric nature of the Gaussian process gives rise to a flexible and diverse class of probabilistic models. There are two main challenges with using Gaussian processes. Their main complication is the computational time which increases rapidly as a function of a number of data points. Other challenge is the analytically intractable inference, which exacerbates the slow computational time. This dissertation considers methods to alleviate these problems. The inference problem is attacked with approximative methods. The Laplace approximation and expectation propagation algorithm are utilized to give Gaussian approximation to the conditional posterior distribution of the latent function given the hyperparameters. The integration over hyperparameters is performed using a Monte Carlo, a grid based, or a central composite design integration. Markov chain Monte Carlo methods over all unknown parameters are used as a golden standard to which the other methods are compared. The rapidly increasing computational time is cured with sparse approximations to Gaussian process and compactly supported covariance functions. These are both analyzed in detail and tested in experiments. Practical details on their implementation with the approximative inference techniques are discussed. The techniques for speeding up the inference are tested in three modeling problems. The problems considered are disease mapping, regression and classification. The disease mapping and regression problems are tackled with standard and robust observation models. The results show that the techniques presented speed up the inference considerably without compromising the accuracy severely.