In this thesis, an adaptive transit routing (ATR) problem in a schedule based stochastic time-dependent transit network is defined and formulated as a finite horizon Markov Decision Process (MDP). The transit link travel times are assumed to be random with known probability distributions. Routing strategies are defined to be conditional on the arrival times at intermediate nodes, and the location and arrival times of other buses in the network. In other words, a traveler in the network decides to walk, wait or board a bus based on the real time information of all buses in the network. The objective is to find a strategy that minimizes the expected travel time, subject to constraints that guarantee that the destination is reached within a certain threshold. The value of the threshold was chosen to reflect the risk averse attitude of travelers and is computed based on the earliest time by which the destination can be reached with probability 1. The problem inherits the curse of dimensionality and state space reduction through pre-processing is achieved by solving variants of the time dependent shortest path problem. An interesting analogy between the state space reduction techniques and the concept of light cones is discussed. A dynamic program framework to solve the problem is developed by defining the state space, decision space and transition functions. Numerical results on a small instance of the Austin transit network are presented to investigate the extent of reduction in state space using the proposed methods. / text

We define a subclass of dynamic linear models with unknown hyperparameters called d-inverse-gamma models. We then approximate the marginal p.d.f.s of the hyperparameter and the state vector by the data augmentation algorithm of Tanner/Wong. We prove that the regularity conditions for convergence hold. A sampling based scheme for practical implementation is discussed. Finally, we illustrate how to obtain an iterative importance sampling estimate of the model likelihood. (author's abstract) / Series: Forschungsberichte / Institut für Statistik

Sequential Monte Carlo (SMC) and Forward Filtering Backward Sampling (FFBS) are the two most often seen algorithms for Bayesian state space models analysis. Various results regarding the applicability has been either claimed or shown. It is said that SMC would excel under nonlinear, non-Gaussian situations, and less computationally expansive. On the other hand, it has been shown that with techniques such as Grid approximation (Hore et al. 2010), FFBS based methods would do no worse, though still can be computationally expansive, but provide more exact information. The purpose of this report to compare the two methods with simulated data sets, and further explore whether there exist some clear criteria that may be used to determine a priori which methods would suit the study better. / text

The purpose of this thesis was to examine parent-adolescent relationships during the transition to high school. Fourteen parent-adolescent dyads from two Canadian cities completed the study. At pre-transition (Time 1) and post-transition (Time 2) to high school, each dyad was video-recorded engaging in a conversation together about various topics related to the school transition. Content analysis was conducted to explore parents’ and adolescents’ pre-transition anticipations of how their relationship would be in high school. State space grid analysis was used to investigate whether there were changes in the structure or emotional content of parent-adolescent dyadic interactions between pre-transition and post-transition to high school. Results showed that parents and adolescents expressed relationship anticipations of stability, change, or uncertainty. Additionally, no statistically significant differences were found in the structure or emotional content of parent-adolescent interactions, suggesting that the high school transition does not appear to disrupt how parents and adolescents interact together. / Social Sciences and Humanities Research Council (SSHRC) and Ontario Graduate Scholarship (OGS)

The purpose of this study was to compare the coach-athlete interaction structures of two competitive youth synchronized swimming teams, one more successful with regard to athletes’ performance and personal development than the other. This comparison was conducted through the first application of state space grid (SSG) observational methodology (Hollenstein, 2007; Lewis, Lamey, & Douglas, 1999) in field-based sport psychology research. Both teams (two head coaches and 17 athletes in total) were observed over multiple training sessions. Both coach and athlete behaviour was coded continuously for the duration of each training session. Measures of coach athlete interaction structure, based on dynamic systems concepts, were derived from these coded behaviours and compared between teams. Results revealed significant differences between the two teams on measures of interaction variability, behavioural content patterns, and the sequencing of coach behaviours. The more successful team was characterized by less variable, more patterned interactions between coaches and athletes. This patterning took the form of more individualized technical and positive reinforcement feedback information and significantly less use of negative feedback by the head coach, interspersed with substantial periods of silent observation. The athletes of the more successful team more actively acknowledged the receipt of this feedback from their coach. The sequencing of coach behaviours was more patterned for the coach of the more successful team, with heavy emphasis on the pairing of technical correction and positive reinforcement statements. The findings suggest that a respectful, deliberate pattern of coach-athlete interaction may be associated with youth sport environments producing more positive performance and personal development outcomes for athletes. / Thesis (Master, Kinesiology & Health Studies) -- Queen's University, 2009-12-16 14:46:55.016

Spatial variability of soil properties complicates the understanding of water and solute transport at the field scale. This study evaluated the impact of land use, soil surface roughness, and rainfall characteristics on water transport and Br- leaching under field conditions by means of a new experimental design employing scale-dependent treatment distribution. On a transect with two land use systems, i.e., cropland and grassland, rainfall intensity and the time delay between Br- application and subsequent rainfall were arranged in a periodically repetitive pattern at two different scales. Both scales were distinct from the scale of surface roughness as described by elevation variance. Nests of tensiometers and suction probes were installed at 1-m intervals along the transect to monitor matric potentials and Br- concentrations at different depths, respectively. After rainfall simulation, soil samples were collected at every 0.5 m horizontal distance in 10 cm vertical increments down to 1 m depth for Br- analysis. Soil Br- concentration was more evenly distributed with soil depth and leached deeper in grassland than cropland, owing to vertically continuous macropores that supported preferential flow. Frequency-domain analysis and autoregressive state-space approach revealed that the dominant factors controlling Br- leaching varied with depth. In shallow layers, land use was the main driving force for Br- distribution. Beyond that, the spatial pattern of Br- was mostly affected by rainfall characteristics. Below 40 cm, the horizontal distribution of Br- was dominated by soil texture and to a smaller extent by rainfall intensity. Bromide concentrations obtained from soil solution samples that were collected through suction probes showed similar results with respect to the influence of rainfall intensity. The spatial variation scale of temporal matric potential change varied with both time and depth, corresponding to different boundary condition scales. Matric potential change in some cases, reflected the impact of soil properties other than the boundary conditions investigated, such as hydraulic conductivity, contributing to the scale-variant behavior of Br- leaching. These findings suggest the applicability of scale-dependent treatment distribution in designing field experiments and also hold important implications for agricultural management and hydrological modelling.

The work presented in this thesis was motivated by the desire to establish an alternative approach to the design of robust polynomial controllers. The procedure of pole-placement forms the basis of the design and for polynomial systems this generally involves the solution of a diophantine equation. This equation has many possible solutions which leads directly to the idea of determining the most appropriate solution for improved performance robustness. A thorough review of many of the aspects of the diophantine equation is presented, which helps to gain an understanding of this extremely important equation. A basic investigation into selecting a more robust solution is carried out but it is shown that, in the polynomial framework, it is difficult to relate decisions in the design procedure to the effect on performance robustness. This leads to the approach of using a state space based design and transforming the resulting output feedback controller to polynomial form. The state space design is centred around parametric output feedback which explicitly represents a set of possible feedback controllers in terms of arbitrary free parameters. The aim is then to select these free parameters such that the closed-loop system has improved performance robustness. Two parametric methods are considered and compared, one being well established and the other a recently proposed scheme. Although the well established method performs slightly better for general systems it is shown to fail when applied to this type of problem. For performance robustness, the shape of the transient response in the presence of model uncertainty is of interest. It is well known that the eigenvalues and eigenvectors play an important role in determining the transient behaviour and as such the sensitivities of these factors to model uncertainty forms the basis on which the free parameters are selected. Numerical optimisation is used to select the free parameters such that the sensitivities are at a minimum. It is shown both in a simple example and in a more realistic application that a significant improvement in the transient behaviour in the presence of model uncertainty can be achieved using the proposed design procedure.

It is suggested to discriminate between different state space models for a given time series by means of a Bayesian approach which chooses the model that minimizes the expected loss. Practical implementation of this procedures requires a fully Bayesian analysis for both the state vector and the unknown hyperparameters which is carried out by Markov chain Monte Carlo methods. Application to some non-standard situations such as testing hypotheses on the boundary of the parameter space, discriminating non-nested models and discrimination of more than two models is discussed in detail. (author's abstract) / Series: Forschungsberichte / Institut für Statistik

Thesis (M.S.)--University of Texas at El Paso, 2007. / Title from title screen. Vita. CD-ROM. Includes bibliographical references. Also available online.

Thesis (Ph.D.) -- University of Western Sydney, 2003. / A thesis submitted for the degree of Doctor of Philosophy, Management, University of Western Sydney, 2003. Bibliography : leaves 215-234.