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Publications (10 of 31) Show all publications
Nordström, M., Soderberg, J., Shusharina, N., Edmunds, D., Lofman, F., Hult, H., . . . Bortfeld, T. (2019). Interactive Deep Learning-Based Delineation of Gross Tumor Volume for Postoperative Glioma Patients. Paper presented at Annual Meeting of the American-Association-of-Physicists-in-Medicine (AAPM), JUL 14-18, 2019, San Antonio, TX. Medical physics (Lancaster), 46(6), E426-E427
Open this publication in new window or tab >>Interactive Deep Learning-Based Delineation of Gross Tumor Volume for Postoperative Glioma Patients
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2019 (English)In: Medical physics (Lancaster), ISSN 0094-2405, Vol. 46, no 6, p. E426-E427Article in journal, Meeting abstract (Other academic) Published
Place, publisher, year, edition, pages
WILEY, 2019
National Category
Other Physics Topics
Identifiers
urn:nbn:se:kth:diva-255208 (URN)000471277703114 ()
Conference
Annual Meeting of the American-Association-of-Physicists-in-Medicine (AAPM), JUL 14-18, 2019, San Antonio, TX
Note

QC 20190904

Available from: 2019-09-04 Created: 2019-09-04 Last updated: 2019-09-04Bibliographically approved
Nordström, M., Hult, H., Maki, A. & Löfman, F. (2018). Pareto Dose Prediction Using Fully Convolutional Networks Operating in 3D. Paper presented at 60th Annual Meeting of the American-Association-of-Physicists-in-Medicine, JUL 29-AUG 02, 2018, Nashville, TN. Medical physics (Lancaster), 45(6), E176-E176
Open this publication in new window or tab >>Pareto Dose Prediction Using Fully Convolutional Networks Operating in 3D
2018 (English)In: Medical physics (Lancaster), ISSN 0094-2405, Vol. 45, no 6, p. E176-E176Article in journal, Meeting abstract (Other academic) Published
Place, publisher, year, edition, pages
WILEY, 2018
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:kth:diva-232417 (URN)10.1002/mp.12938 (DOI)000434978000213 ()
Conference
60th Annual Meeting of the American-Association-of-Physicists-in-Medicine, JUL 29-AUG 02, 2018, Nashville, TN
Note

QC 20180726

Available from: 2018-07-26 Created: 2018-07-26 Last updated: 2018-07-26Bibliographically approved
Hult, H. & Nyquist, P. (2016). Large deviations for weighted empirical measures arising in importance sampling. Stochastic Processes and their Applications, 126(1)
Open this publication in new window or tab >>Large deviations for weighted empirical measures arising in importance sampling
2016 (English)In: Stochastic Processes and their Applications, ISSN 0304-4149, E-ISSN 1879-209X, Vol. 126, no 1Article in journal (Refereed) Published
Abstract [en]

Importance sampling is a popular method for efficient computation of various properties of a distribution such as probabilities, expectations, quantiles etc. The output of an importance sampling algorithm can be represented as a weighted empirical measure, where the weights are given by the likelihood ratio between the original distribution and the sampling distribution. In this paper the efficiency of an importance sampling algorithm is studied by means of large deviations for the weighted empirical measure. The main result, which is stated as a Laplace principle for the weighted empirical measure arising in importance sampling, can be viewed as a weighted version of Sanov's theorem. The main theorem is applied to quantify the performance of an importance sampling algorithm over a collection of subsets of a given target set as well as quantile estimates. The proof of the main theorem relies on the weak convergence approach to large deviations developed by Dupuis and Ellis.

Place, publisher, year, edition, pages
Elsevier, 2016
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:kth:diva-117805 (URN)10.1016/j.spa.2015.08.002 (DOI)000366535500006 ()2-s2.0-84948440031 (Scopus ID)
Note

QC 20160115

Available from: 2013-02-05 Created: 2013-02-05 Last updated: 2017-12-06Bibliographically approved
Gudmundsson, T. & Hult, H. (2014). Markov chain monte carlo for computing rare-event probabilities for a heavy-tailed random walk. Journal of Applied Probability, 51(2), 359-376
Open this publication in new window or tab >>Markov chain monte carlo for computing rare-event probabilities for a heavy-tailed random walk
2014 (English)In: Journal of Applied Probability, ISSN 0021-9002, E-ISSN 1475-6072, Vol. 51, no 2, p. 359-376Article in journal (Refereed) Published
Abstract [en]

In this paper a method based on a Markov chain Monte Carlo (MCMC) algorithm is proposed to compute the probability of a rare event. The conditional distribution of the underlying process given that the rare event occurs has the probability of the rare event as its normalizing constant. Using the MCMC methodology, a Markov chain is simulated, with the aforementioned conditional distribution as its invariant distribution, and information about the normalizing constant is extracted from its trajectory. The algorithm is described in full generality and applied to the problem of computing the probability that a heavy-tailed random walk exceeds a high threshold. An unbiased estimator of the reciprocal probability is constructed whose normalized variance vanishes asymptotically. The algorithm is extended to random sums and its performance is illustrated numerically and compared to existing importance sampling algorithms.

Place, publisher, year, edition, pages
Applied Probability Trust, 2014
Keywords
Markov chain Monte Carlo, heavy tail, rare-event simulation, random walk
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:kth:diva-136800 (URN)10.1239/jap/1402578630 (DOI)000338269000005 ()2-s2.0-84904006393 (Scopus ID)
Note

QC 20140806

Research funded by Göran Gustafsson's foundation

Available from: 2013-12-09 Created: 2013-12-09 Last updated: 2017-12-06Bibliographically approved
Hult, H., Lindskog, F. & Nykvist, J. (2013). A simple time-consistent model for the forward density process. International Journal of Theoretical and Applied Finance, 16(8), 13500489
Open this publication in new window or tab >>A simple time-consistent model for the forward density process
2013 (English)In: International Journal of Theoretical and Applied Finance, ISSN 0219-0249, Vol. 16, no 8, p. 13500489-Article in journal (Refereed) Published
Abstract [en]

In this paper, a simple model for the evolution of the forward density of the future value of an asset is proposed. The model allows for a straightforward initial calibration to option prices and has dynamics that are consistent with empirical findings from option price data. The model is constructed with the aim of being both simple and realistic, and avoid the need for frequent re-calibration. The model prices of n options and a forward contract are expressed as time-varying functions of an (n + 1)-dimensional Brownian motion and it is investigated how the Brownian trajectory can be determined from the trajectories of the price processes. An approach based on particle filtering is presented for determining the location of the driving Brownian motion from option prices observed in discrete time. A simulation study and an empirical study of call options on the S&P 500 index illustrate that the model provides a good fit to option price data.

Keywords
mixture models, Option pricing
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:kth:diva-136972 (URN)10.1142/S0219024913500489 (DOI)2-s2.0-84892834759 (Scopus ID)
Note

QC 20140319. Updated from accepted to published.

Available from: 2013-12-10 Created: 2013-12-10 Last updated: 2017-12-06Bibliographically approved
Blanchet, J., Hult, H. & Leder, K. (2013). Rare-Event Simulation for Stochastic Recurrence Equations with Heavy-Tailed Innovations. ACM Transactions on Modeling and Computer Simulation, 23(4), 22
Open this publication in new window or tab >>Rare-Event Simulation for Stochastic Recurrence Equations with Heavy-Tailed Innovations
2013 (English)In: ACM Transactions on Modeling and Computer Simulation, ISSN 1049-3301, E-ISSN 1558-1195, Vol. 23, no 4, p. 22-Article in journal (Refereed) Published
Abstract [en]

In this article, rare-event simulation for stochastic recurrence equations of the form Xn+1 = A(n+1)X(n) + Bn+1, X-0 = 0 is studied, where {A(n);n >= 1} and {B-n;n >= 1} are independent sequences consisting of independent and identically distributed real-valued random variables. It is assumed that the tail of the distribution of B-1 is regularly varying, whereas the distribution of A(1) has a suitably light tail. The problem of efficient estimation, via simulation, of quantities such as P{X-n > b} and P{sup(k <= n) X-k > b} for large b and n is studied. Importance sampling strategies are investigated that provide unbiased estimators with bounded relative error as b and n tend to infinity.

Keywords
Importance sampling, stochastic recurrence equations, heavy-tails
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:kth:diva-136970 (URN)10.1145/2517451 (DOI)000329124400002 ()
Note

QC 20140123. Updated from accepted to published.

Research supported by the Göran Gustafsson Foundation. 

Available from: 2013-12-10 Created: 2013-12-10 Last updated: 2017-12-06Bibliographically approved
Hult, H. & Svensson, J. (2012). On Importance Sampling with Mixtures for Random Walks with Heavy Tails. ACM Transactions on Modeling and Computer Simulation, 22(2), 8
Open this publication in new window or tab >>On Importance Sampling with Mixtures for Random Walks with Heavy Tails
2012 (English)In: ACM Transactions on Modeling and Computer Simulation, ISSN 1049-3301, E-ISSN 1558-1195, Vol. 22, no 2, p. 8-Article in journal (Refereed) Published
Abstract [en]

State-dependent importance sampling algorithms based on mixtures are considered. The algorithms are designed to compute tail probabilities of a heavy-tailed random walk. The increments of the random walk are assumed to have a regularly varying distribution. Sufficient conditions for obtaining bounded relative error are presented for rather general mixture algorithms. Two new examples, called the generalized Pareto mixture and the scaling mixture, are introduced. Both examples have good asymptotic properties and, in contrast to some of the existing algorithms, they are very easy to implement. Their performance is illustrated by numerical experiments. Finally, it is proved that mixture algorithms of this kind can be designed to have vanishing relative error.

Keywords
Rare event simulation, heavy tails, importance sampling
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:kth:diva-11269 (URN)10.1145/2133390.2133392 (DOI)000302131400002 ()2-s2.0-84859453675 (Scopus ID)
Funder
Swedish Research Council, 621-2008-4944
Note
QC 20100811Available from: 2009-10-13 Created: 2009-10-13 Last updated: 2017-12-12Bibliographically approved
Lindskog, F., Hult, H., Hammarlid, O. & Rehn, C.-J. (2012). Risk and portfolio analysis: principles and methods. Springer-Verlag New York
Open this publication in new window or tab >>Risk and portfolio analysis: principles and methods
2012 (English)Book (Refereed)
Place, publisher, year, edition, pages
Springer-Verlag New York, 2012
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:kth:diva-137987 (URN)978-1-4614-4102-1 (ISBN)978-1-4614-4103-8 (ISBN)
Note

QC 20140829

Available from: 2013-12-17 Created: 2013-12-17 Last updated: 2020-01-03Bibliographically approved
Blanchet, J., Hult, H. & Leder, K. (2011). Importance sampling for stochastic recurrence equations with heavy-tailed increments. In: Proceedings of the 2011 Winter Simulation Conference: . Paper presented at 2011 Winter Simulation Conference. Phoenix, US. 11-14 Dec. 2011 (pp. 3824-3831).
Open this publication in new window or tab >>Importance sampling for stochastic recurrence equations with heavy-tailed increments
2011 (English)In: Proceedings of the 2011 Winter Simulation Conference, 2011, p. 3824-3831Conference paper, Published paper (Other academic)
Abstract [en]

Importance sampling in the setting of heavy tailed random variables has generally focused on models withadditive noise terms. In this work we extend this concept by considering importance sampling for theestimation of rare events in Markov chains of the formXn+1 = An+1Xn+Bn+1; X0 = 0;where the Bn’s and An’s are independent sequences of independent and identically distributed (i.i.d.) randomvariables and the Bn’s are regularly varying and the An’s are suitably light tailed relative to Bn. We focuson efficient estimation of the rare event probability P(Xn > b) as b%¥. In particular we present a stronglyefficient importance sampling algorithm for estimating these probabilities, and present a numerical exampleshowcasing the strong efficiency.

National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:kth:diva-84217 (URN)10.1109/WSC.2011.6148074 (DOI)000300520804013 ()2-s2.0-84858031220 (Scopus ID)
Conference
2011 Winter Simulation Conference. Phoenix, US. 11-14 Dec. 2011
Note

QC 20120410

Available from: 2012-02-13 Created: 2012-02-13 Last updated: 2020-02-18Bibliographically approved
Hult, H. & Lindskog, F. (2011). Ruin probabilities under general investments and heavy-tailed claims. Finance and Stochastics, 15(2), 243-265
Open this publication in new window or tab >>Ruin probabilities under general investments and heavy-tailed claims
2011 (English)In: Finance and Stochastics, ISSN 0949-2984, E-ISSN 1432-1122, Vol. 15, no 2, p. 243-265Article in journal (Refereed) Published
Abstract [en]

In this paper, the asymptotic decay of finite time ruin probabilities is studied. An insurance company is considered that faces heavy-tailed claims and makes investments in risky assets whose prices evolve according to quite general semimartingales. In this setting, the ruin problem corresponds to determining hitting probabilities for the solution to a randomly perturbed stochastic integral equation. A large deviation result for the hitting probabilities is derived that holds uniformly over a family of semimartingales. This result gives the asymptotic decay of finite time ruin probabilities under sufficiently conservative investment strategies, including ruin-minimizing strategies. In particular, as long as the insurance company invests sufficiently conservatively, the investment strategy has only a moderate impact on the asymptotics of the ruin probability.

Keywords
Ruin probabilities, Heavy tails, Large deviations
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:kth:diva-34670 (URN)10.1007/s00780-010-0135-7 (DOI)000290573800003 ()2-s2.0-79955933889 (Scopus ID)
Note
QC 20110614Available from: 2011-06-14 Created: 2011-06-13 Last updated: 2017-12-11Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-9210-121X

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