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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Estimating phylogenetic trees from discrete morphological data

Wright, April Marie 04 September 2015 (has links)
Morphological characters have a long history of use in the estimation of phylogenetic trees. Datasets consisting of morphological characters are most often analyzed using the maximum parsimony criterion, which seeks to minimize the amount of character change across a phylogenetic tree. When combined with molecular data, characters are often analyzed using model-based methods, such as maximum likelihood or, more commonly, Bayesian estimation. The efficacy of likelihood and Bayesian methods using a common model for estimating topology from discrete morphological characters, the Mk model, is poorly-explored. In Chapter One, I explore the efficacy of Bayesian estimation of phylogeny, using the Mk model, under conditions that are commonly encountered in paleontological studies. Using simulated data, I describe the relative performances of parsimony and the Mk model under a range of realistic conditions that include common scenarios of missing data and rate heterogeneity. I further examine the use of the Mk model in Chapter Two. Like any model, the Mk model makes a number of assumptions. One is that transition between character states are symmetric (i.e., there is an equal probability of changing from state 0 to state 1 and from state 1 to state 0). Many characters, including alleged Dollo characters and extremely labile characters, may not fit this assumption. I tested methods for relaxing this assumption in a Bayesian context. Using empirical datasets, I performed model fitting to demonstrate cases in which modelling asymmetric transitions among characters is preferred. I used simulated datasets to demonstrate that choosing the best-fit model of transition state symmetry can improve model fit and phylogenetic estimation. In my final chapter, I looked at the use of partitions to model datasets more appropriately. Common in molecular studies, partitioning breaks up the dataset into pieces that evolve according to similar mechanisms. These pieces, called partitions, are then modeled separately. This practice has not been widely adopted in morphological studies. I extended the PartitionFinder software, which is used in molecular studies to score different possible partition schemes to find the one which best models the dataset. I used empirical datasets to demonstrate the effects of partitioning datasets on model likelihoods and on the phylogenetic trees estimated from those datasets. / text
2

Parallel Stochastic Estimation on Multicore Platforms

Rosén, Olov January 2015 (has links)
The main part of this thesis concerns parallelization of recursive Bayesian estimation methods, both linear and nonlinear such. Recursive estimation deals with the problem of extracting information about parameters or states of a dynamical system, given noisy measurements of the system output and plays a central role in signal processing, system identification, and automatic control. Solving the recursive Bayesian estimation problem is known to be computationally expensive, which often makes the methods infeasible in real-time applications and problems of large dimension. As the computational power of the hardware is today increased by adding more processors on a single chip rather than increasing the clock frequency and shrinking the logic circuits, parallelization is one of the most powerful ways of improving the execution time of an algorithm. It has been found in the work of this thesis that several of the optimal filtering methods are suitable for parallel implementation, in certain ranges of problem sizes. For many of the suggested parallelizations, a linear speedup in the number of cores has been achieved providing up to 8 times speedup on a double quad-core computer. As the evolution of the parallel computer architectures is unfolding rapidly, many more processors on the same chip will soon become available. The developed methods do not, of course, scale infinitely, but definitely can exploit and harness some of the computational power of the next generation of parallel platforms, allowing for optimal state estimation in real-time applications. / CoDeR-MP
3

A Bayesian inversion framework for subsurface seismic imaging problems

Urozayev, Dias 11 1900 (has links)
This thesis considers the reconstruction of subsurface models from seismic observations, a well-known high-dimensional and ill-posed problem. As a first regularization to such a problem, a reduction of the parameters' space is considered following a truncated Discrete Cosine Transform (DCT). This helps regularizing the seismic inverse problem and alleviates its computational complexity. A second regularization based on Laplace priors as a way of accounting for sparsity in the model is further proposed to enhance the reconstruction quality. More specifically, two Laplace-based penalizations are applied: one for the DCT coefficients and another one for the spatial variations of the subsurface model, which leads to an enhanced representation of cross-correlations of the DCT coefficients. The Laplace priors are represented by hierarchical forms that are suitable for deriving efficient inversion schemes. The corresponding inverse problem, which is formulated within a Bayesian framework, lies in computing the joint posteriors of the target model parameters and the hyperparameters of the introduced priors. Such a joint posterior is indeed approximated using the Variational Bayesian (VB) approach with a separable form of marginals under the minimization of Kullback-Leibler divergence criterion. The VB approach can provide an efficient means of obtaining not only point estimates but also closed forms of the posterior probability distributions of the quantities of interest, in contrast with the classical deterministic optimization methods. The case in which the observations are contaminated with outliers is further considered. For that case, a robust inversion scheme is proposed based on a Student-t prior for the observation noise. The proposed approaches are applied to successfully reconstruct the subsurface acoustic impedance model of the Volve oilfield.
4

An estimated two-country DSGE model of Austria and the Euro Area

Breuss, Fritz, Rabitsch, Katrin January 2008 (has links) (PDF)
We present a two-country New Open Economy Macro model of the Austrian economy within the European Union's Economic & Monetary Union (EMU). The model includes both nominal and real frictions that have proven to be important in matching business cycle facts, and that allows for an investigation of the effects and cross-country transmission of a number of structural shocks: shocks to technologies, shocks to preferences, cost-push type shocks and policy shocks. The model is estimated using Bayesian methods on quarterly data covering the period of 1976:Q1- 2005:Q1. In addition to the assessment of the relative importance of various shocks, the model also allows to investigate effects of the monetary regime switch with the final stage of the EMU and investigates in how far this has altered macroeconomic transmission. We find that Austria's economy appears to react stronger to demand shocks, while in the rest of the Euro Area supply shocks have a stronger impact. Comparing the estimations on pre-EMU and EMU subsamples we find that the contribution of (rest of the) Euro Area shocks to Austria's business cycle fluctuations has increased significantly. (author´s abstract) / Series: EI Working Papers / Europainstitut
5

Reservoir History Matching Using Ensemble Kalman Filters with Anamorphosis Transforms

Aman, Beshir M. 12 1900 (has links)
This work aims to enhance the Ensemble Kalman Filter performance by transforming the non-Gaussian state variables into Gaussian variables to be a step closer to optimality. This is done by using univariate and multivariate Box-Cox transformation. Some History matching methods such as Kalman filter, particle filter and the ensemble Kalman filter are reviewed and applied to a test case in the reservoir application. The key idea is to apply the transformation before the update step and then transform back after applying the Kalman correction. In general, the results of the multivariate method was promising, despite the fact it over-estimated some variables.
6

Belief driven autonomous manipulator pose selection for less controlled environments

Webb, Stephen Scott, Mechanical & Manufacturing Engineering, Faculty of Engineering, UNSW January 2008 (has links)
This thesis presents a new approach for selecting a manipulator arm configuration (a pose) in an environment where the positions of the work items are not able to be fully controlled. The approach utilizes a belief formed from a priori knowledge, observations and predictive models to select manipulator poses and motions. Standard methods for manipulator control provide a fully specified Cartesian pose as the input to a robot controller which is assumed to act as an ideal Cartesian motion device. While this approach simplifies the controller and makes it more portable, it is not well suited for less-controlled environments where the work item position or orientation may not be completely observable and where a measure of the accuracy of the available observations is required. The proposed approach suggests selecting a manipulator configuration using two types of rating function. When uncertainty is high, configurations are rated by combining a belief, represented by a probability density function, and a value function in a decision theoretic manner enabling selection of the sensor??s motion based on its probabilistic contribution to information gain. When uncertainty is low the mean or mode of the environment state probability density function is utilized in task specific linear or angular distances constraints to map a configuration to a cost. The contribution of this thesis is in providing two formulations that allow joint configurations to be found using non-linear optimization algorithms. The first formulation shows how task specific linear and angular distance constraints are combined in a cost function to enable a satisfying pose to be selected. The second formulation is based on the probabilistic belief of the predicted environment state. This belief is formed by utilizing a Bayesian estimation framework to combine the a priori knowledge with the output of sensor data processing, a likelihood function over the state space, thereby handling the uncertainty associated with sensing in a less controlled environment. Forward models are used to transform the belief to a predicted state which is utilized in motion selection to provide the benefits of a feedforward control strategy. Extensive numerical analysis of the proposed approach shows that using the fed-forward belief improves tracking performance by up to 19%. It is also shown that motion selection based on the dynamically maintained belief reduces time to target detection by up to 50% compared to two other control approaches. These and other results show how the proposed approach is effectively able to utilize an uncertain environment state belief to select manipulator arm configurations.
7

Spatially reconfigurable and non-parametric representation of dynamic bayesian beliefs

Lavis, Benjamin Mark, Mechanical & Manufacturing Engineering, Faculty of Engineering, UNSW January 2008 (has links)
This thesis presents a means for representing and computing beliefs in the form of arbitrary probability density functions with a guarantee for the ongoing validity of such beliefs over indefinte time frames. The foremost aspect of this proposal is the introduction of a general, theoretical, solution to the guaranteed state estimation problem from within the recursive Bayesian estimation framework. The solution presented here determines the minimum space required, at each stage of the estimation process, to represent the belief with limited, or no, loss of information. Beyond this purely theoretical aspect, a number of numerical techniques, capable of determining the required space and performing the appropriate spatial reconfiguration, whilst also computing and representing the belief functions, are developed. This includes a new, hybrid particle-element approach to recursive Bayesian estimation. The advantage of spatial reconfiguration as presented here is that it ensures that the belief functions consider all plausible states of the target system, without altering the recursive Bayesian estimation equations used to form those beliefs. Furthermore, spatial reconfiguration as proposed in this dissertation enhances the estimation process since it allows computational resources to be concentrated on only those states considered plausible. Autonomous maritime search and rescue is used as a focus application throughout this dissertation since the searching-and-tracking requirements of the problem involve uncertainty, the use of arbitrary belief functions and dynamic target systems. Nevertheless, the theoretical development in this dissertation has been kept general and independent of an application, and as such the theory and techniques presented here may be applied to any problem involving dynamic Bayesian beliefs. A number of numerical experiments and simulations show the efficacy of the proposed spatially reconfigurable representations, not only in ensuring the validity of the belief functions over indefinite time frames, but also in reducing computation time and improving the accuracy of function approximation. Improvements of an order of magnitude were achieved when compared with traditional, spatially static representations.
8

Financial Intermediation and the Macroeconomy of the United States: Quantitative Assessments

Chiu, Ching Wai January 2012 (has links)
<p>This dissertation presents a quantitative study on the relationship between financial intermediation and the macroeconomy of the United States. It consists of two major chapters, with the first chapter studying adverse shocks to interbank market lending, and with the second chapter studying a theoretical model where aggregate balance sheets of the financial and non-financial sectors play a key role in financial intermediation frictions.</p><p>In the first chapter, I empirically investigate a novel macroeconomic shock: the funding liquidity shock. Funding liquidity is defined as the ability of a (financial) institution to raise cash at short notice, with interbank market loans being a very common source of short-term external funding. Using the "TED spread" as a proxy of aggregate funding liquidity for the period from 1971M1 to 2009M9, I first discover that, by using the vector-autoregression approach, an unanticipated adverse TED shock brings significant recessionary effects: industrial production and prices fall, and the unemployment rate rises. The contraction lasts for about twenty months. I also recover the conventional monetary policy shock, the macro impact of which is in line with the results of Christiano et al (1998) and Christiano et al (2005) . I then follow the factor model approach and find that the excess returns of small-firm portfolios are more negatively impacted by an adverse funding liquidity shock. I also present evidence that this shock as a "risk factor" is priced in the cross-section of equity returns. Moreover, a proposed factor model which includes the structural funding liquidity and monetary policy shocks as factors is able to explain the cross-sectional returns of portfolios sorted on size and book-to-market ratio as well as the Fama and French (1993) three-factor model does. Lastly, I present empirical evidence that funding liquidity and market liquidity mutually affect each other.</p><p>I start the second chapter by showing that, in U.S. data, the balance sheet health of the financial sector, as measured by its equity capital and debt level, is a leading indicator of the balance sheet health of the nonfinancial sector. This fact, and the apparent role of the financial sector in the recent global financial crisis, motivate a general equilibrium macroeconomic model featuring the balance sheets of both sectors. I estimate and study a model within the "loanable funds" framework of Holmstrom and Tirole (1997), which introduces a double moral hazard problem in the financial intermediation process. I find that financial frictions modeled within this framework give rise to a shock transmission mechanism quantitatively different from the one that arises with the conventional modeling assumption, in New Keynesian business cycle models, of convex investment adjustment costs. Financial equity capital plays an important role in determining the depth and persistence of declines in output and investment due to negative shocks to the economy. Moreover, I find that shocks to the financial intermediation process cause persistent recessions, and that these shocks explain a significant portion of the variation in investment. The estimated model is also able to replicate some aspects of the cross-correlation structure of the balance sheet variables of the two sectors.</p> / Dissertation
9

Belief driven autonomous manipulator pose selection for less controlled environments

Webb, Stephen Scott, Mechanical & Manufacturing Engineering, Faculty of Engineering, UNSW January 2008 (has links)
This thesis presents a new approach for selecting a manipulator arm configuration (a pose) in an environment where the positions of the work items are not able to be fully controlled. The approach utilizes a belief formed from a priori knowledge, observations and predictive models to select manipulator poses and motions. Standard methods for manipulator control provide a fully specified Cartesian pose as the input to a robot controller which is assumed to act as an ideal Cartesian motion device. While this approach simplifies the controller and makes it more portable, it is not well suited for less-controlled environments where the work item position or orientation may not be completely observable and where a measure of the accuracy of the available observations is required. The proposed approach suggests selecting a manipulator configuration using two types of rating function. When uncertainty is high, configurations are rated by combining a belief, represented by a probability density function, and a value function in a decision theoretic manner enabling selection of the sensor??s motion based on its probabilistic contribution to information gain. When uncertainty is low the mean or mode of the environment state probability density function is utilized in task specific linear or angular distances constraints to map a configuration to a cost. The contribution of this thesis is in providing two formulations that allow joint configurations to be found using non-linear optimization algorithms. The first formulation shows how task specific linear and angular distance constraints are combined in a cost function to enable a satisfying pose to be selected. The second formulation is based on the probabilistic belief of the predicted environment state. This belief is formed by utilizing a Bayesian estimation framework to combine the a priori knowledge with the output of sensor data processing, a likelihood function over the state space, thereby handling the uncertainty associated with sensing in a less controlled environment. Forward models are used to transform the belief to a predicted state which is utilized in motion selection to provide the benefits of a feedforward control strategy. Extensive numerical analysis of the proposed approach shows that using the fed-forward belief improves tracking performance by up to 19%. It is also shown that motion selection based on the dynamically maintained belief reduces time to target detection by up to 50% compared to two other control approaches. These and other results show how the proposed approach is effectively able to utilize an uncertain environment state belief to select manipulator arm configurations.
10

Spatially reconfigurable and non-parametric representation of dynamic bayesian beliefs

Lavis, Benjamin Mark, Mechanical & Manufacturing Engineering, Faculty of Engineering, UNSW January 2008 (has links)
This thesis presents a means for representing and computing beliefs in the form of arbitrary probability density functions with a guarantee for the ongoing validity of such beliefs over indefinte time frames. The foremost aspect of this proposal is the introduction of a general, theoretical, solution to the guaranteed state estimation problem from within the recursive Bayesian estimation framework. The solution presented here determines the minimum space required, at each stage of the estimation process, to represent the belief with limited, or no, loss of information. Beyond this purely theoretical aspect, a number of numerical techniques, capable of determining the required space and performing the appropriate spatial reconfiguration, whilst also computing and representing the belief functions, are developed. This includes a new, hybrid particle-element approach to recursive Bayesian estimation. The advantage of spatial reconfiguration as presented here is that it ensures that the belief functions consider all plausible states of the target system, without altering the recursive Bayesian estimation equations used to form those beliefs. Furthermore, spatial reconfiguration as proposed in this dissertation enhances the estimation process since it allows computational resources to be concentrated on only those states considered plausible. Autonomous maritime search and rescue is used as a focus application throughout this dissertation since the searching-and-tracking requirements of the problem involve uncertainty, the use of arbitrary belief functions and dynamic target systems. Nevertheless, the theoretical development in this dissertation has been kept general and independent of an application, and as such the theory and techniques presented here may be applied to any problem involving dynamic Bayesian beliefs. A number of numerical experiments and simulations show the efficacy of the proposed spatially reconfigurable representations, not only in ensuring the validity of the belief functions over indefinite time frames, but also in reducing computation time and improving the accuracy of function approximation. Improvements of an order of magnitude were achieved when compared with traditional, spatially static representations.

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