Towards Thompson Sampling for Complex Bayesian Reasoning

Paper III, IV, and VI are not available as a part of the dissertation due to the copyright.Thompson Sampling (TS) is a state-of-art algorithm for bandit problems set in a Bayesian framework. Both the theoretical foundation and the empirical efficiency of TS is wellexplored for plain bandit problems. However, the Bayesian underpinning of TS means that TS could potentially be applied to other, more complex, problems as well, beyond the bandit problem, if suitable Bayesian structures can be found. The objective of this thesis is the development and analysis of TS-based schemes for more complex optimization problems, founded on Bayesian reasoning. We address several complex optimization problems where the previous state-of-art relies on a relatively myopic perspective on the problem. These includes stochastic searching on the line, the Goore game, the knapsack problem, travel time estimation, and equipartitioning. Instead of employing Bayesian reasoning to obtain a solution, they rely on carefully engineered rules. In all brevity, we recast each of these optimization problems in a Bayesian framework, introducing dedicated TS based solution schemes. For all of the addressed problems, the results show that besides being more effective, the TS based approaches we introduce are also capable of solving more adverse versions of the problems, such as dealing with stochastic liars.publishedVersio

Accelerated Bayesian learning for decentralized two-armed bandit based decision making with applications to the Goore Game

The two-armed bandit problem is a classical optimization problem where a decision maker sequentially pulls one of two arms attached to a gambling machine, with each pull resulting in a random reward. The reward distributions are unknown, and thus, one must balance between exploiting existing knowledge about the arms, and obtaining new information. Bandit problems are particularly fascinating because a large class of real world problems, including routing, Quality of Service (QoS) control, game playing, and resource allocation, can be solved in a decentralized manner when modeled as a system of interacting gambling machines. Although computationally intractable in many cases, Bayesian methods provide a standard for optimal decision making. This paper proposes a novel scheme for decentralized decision making based on the Goore Game in which each decision maker is inherently Bayesian in nature, yet avoids computational intractability by relying simply on updating the hyper parameters of sibling conjugate priors, and on random sampling from these posteriors. We further report theoretical results on the variance of the random rewards experienced by each individual decision maker. Based on these theoretical results, each decision maker is able to accelerate its own learning by taking advantage of the increasingly more reliable feedback that is obtained as exploration gradually turns into exploitation in bandit problem based learning. Extensive experiments, involving QoS control in simulated wireless sensor networks, demonstrate that the accelerated learning allows us to combine the benefits of conservative learning, which is high accuracy, with the benefits of hurried learning, which is fast convergence. In this manner, our scheme outperforms recently proposed Goore Game solution schemes, where one has to trade off accuracy with speed. As an additional benefit, performance also becomes more stable. We thus believe that our methodology opens avenues for improved performance in a number of applications of bandit based decentralized decision making

Thompson sampling guided stochastic searching on the line for deceptive environments with applications to root-finding problems

publishedVersio

Berre bok gjer ingen klok : korleis arbeider lærarar med motivasjon og leseforståing i den vidare leseopplæringa?

Grunnskulelærarutdanning 1-7 PE369 Mai 201

Muskeldysmorfi blant unge menn -En kvalitativ undersøkelse av deres erfaringer

Sammendrag Hensikten med denne studien var å undersøke hvilke erfaringer menn med muskeldysmorfi har med aspekter knyttet til trening, kosthold og kroppsbilde. Kartlegging med bruk av Muscle Dysmorphic Disorder Inventory (MDDI) ble gjennomført på tre ulike treningssentre for å avdekke symptomer på muskeldysmorfi blant menn og dermed avgjøre hvem som var aktuelle for deltakelse i den kvalitative delen av studien. Fem menn i alderen 18-23 år med et medlemskap på treningssenter ble inkludert og gjennomførte individuelle, semistrukturerte intervjuer. Intervjuene med informantene avdekket flere aspekter som kan ses på som mulige årsaker i utviklingen av lidelsen eller som konsekvenser av lidelsen muskeldysmorfi. Muskeldysmorfi preget de fem informantenes hverdag på ulike måter, som for noen innebar blant annet et tvangsmessig forhold til trening, unngåelse av hendelser hvor kroppen stod i fare for å bli eksponert og utfordringer med kosthold. Denne undersøkelsen viser at muskeldysmorfi kan påvirke personer på ulike måter, med flere uheldige konsekvenser. Fremtidig forskning kan med fordel inkludere flere kvalitative studier som beskriver personers egne erfaringer med lidelsen, samt studier som undersøker mulig behandling av muskeldysmorfi. Nøkkelord: Muskeldysmorfi, kroppsbilde, trening, kosthold, kvalitativ undersøkels

Tsunamigenic Potential of the Baiyun Slide Complex in the South China Sea

The Baiyun slide complex contains geological evidence for some of the largest landslide ever discovered in the continental slopes of the South China Sea. High-resolution seismic data suggest that a variety of landslides with varied scales have occurred repeatedly in this area. The largest landslide reconstructed from bathymetric and seismic data has an estimated spatial coverage of ~5,500 km2 and a conservative volume of ~1,035 km3. Here, using geomorphological and geotechnical data, we construct a series of probable landslide scenarios and assess their tsunamigenic capacity. By treating the slides as deformable mudflows, we simulate the dynamics of landslide movements. The simulated landslide motions match the geophysical observations interpreted in previous studies. Particularly, we are able to reproduce the spatial distribution of observed runout, including the distance, shape, and deposit thickness, for the most credible slide scenario. We investigate tsunami impacts generated by different slide scenarios and highlight the importance of initial water depth, sliding direction, and nearshore bathymetry. The worst-case scenario is capable of producing basin-wide tsunami, with maximum wave amplitudes reaching ~5 m near Hong Kong and Macau, 1–3 m in western Philippines, and at least 1 m along central Vietnam, southeast Hainan, and southern Taiwan. The most noticeable phenomenon we observed is that the southern Chinese coast is the hardest-hit region in all the simulated scenarios regardless of the diverse slide features. We conclude that the persistence of high tsunami impact is caused by the unique bathymetric feature of the wide continental shelf in front of southern China