This thesis investigates the relationship between prosody and formality in spoken Japanese, from the standpoints of both speech production and perception. The previous literature on this topic has often produced inconsistent or contradictory results (e.g. Loveday, 1981; Ofuka at al., 2000; Ito, 2001; Ito, 2002), and this thesis therefore seeks to address the research question of whether speakers and listeners use prosody in any predictable way when expressing or judging formality in spoken Japanese. Chapter 2 describes a pilot study which aimed to determine which prosodic variables were worth investigating in a larger corpus-based study. Speech of different levels of formality was elicited from subjects indirectly via the inclusion of indexical linguistic items in carrier sentences. Analysis of the relationship between mean f₀ and duration shows a significant correlation with the categories of formal and informal speech where both variables are higher in informal speech. Consequently, in Chapter 3 f₀ and articulation rate were analyzed in the corpus-based study. Corpus data for the study was collected via one-on-one conversations recorded at NINJAL in Tachikawa-shi, Japan. The speech data from the corpus was analyzed in order to test the hypothesis that the prosodic variables of mean f₀, articulation rate, and f₀ range would all be consistently higher in informal speech. Analysis using mixed effects models and a functional data analysis shows that all three prosodic variables are significantly higher in informal speech. These results were then used to inform the design of a speech perception study, which tested how manipulation of mean f₀, articulation rate, and f₀ range upwards or downwards affect listeners' judgments of de-lexicalized speech as formal or informal. Results show that manipulation of all three variables upwards or downward leads to listeners' judging recordings as more informal or formal respectively. However, manipulation of individual variables does not have a significant correlation with changes in listeners' judgements. This result led to the theory that categorization tasks in speech perception are probabilistic, with listeners accessing distributions of acoustic cues to the categories in order to make judgments. Chapter 5 of the thesis describes a probabilistic Bayesian model of formality formulated based on the theory of the cognitive process of category judgment described in Chapter 4, which attempts to predict a recording's level of formality based only on its prosody. Given information on the overall and speaker-specific distributions of the prosodic cues to the different levels of formality, the model is able to discriminate between categories at a rate better than chance (~63% accurate for formal speech, ~74% accurate for informal speech), performing better than human listeners - who could not predict formality based on only prosodic information at a rate above chance in the study in Chapter 4. The studies in this thesis show a consistent, significant relationship between prosody and formality in spoken Japanese in both speech production and perception, which can be modeled probabilistically using a Bayesian statistical framework.

Two new spatio-temporal hidden Markov models (HMM) are introduced in this thesis, with the purpose of capturing the persistent, spatially non-homogeneous nature of climate influence on annual rainfall series observed in Australia. The models extend the two-state HMM applied by Thyer (2001) by relaxing the assumption that all sites are under the same climate control. The Switch HMM (SHMM) allows at-site anomalous states, whilst still maintaining a regional control. The Regional HMM (RHMM), on the other hand, allows sites to be partitioned into different Markovian state regions. The analyses were conducted using a Bayesian framework to explicitly account for parameter uncertainty and select between competing hypotheses. Bayesian model averaging was used for comparison of the HMM and its generalisations. The HMM, SHMM and RHMM were applied to four groupings of four sites located on the Eastern coast of Australia, an area that has previously shown evidence of interannual persistence. In the majority of case studies, the RHMM variants showed greatest posterior weight, indicating that the data favoured the multiple region RHMM over the single region HMM or the SHMM variants. In no cases does the HMM produce the maximum marginal likelihood when compared to the SHMM and RHMM. The HMM state series and preferred model variants were sensitive to the parameterisation of the small-scale site-to-site correlation structure. Several parameterisations of the small-scale Gaussian correlation were trialled, namely Fitted Correlation, Exponential Decay Correlation, Empirical and Zero Correlation. Significantly, it was shown that annual rainfall data outliers can have a large effect on inference for a model that uses Gaussian distributions. The practical value of this modelling is demonstrated by the conditioning of the event based point rainfall model DRIP on the hidden state series of the HMM variants. Short timescale models typically underestimate annual variability because there is no explicit structure to incorporate long-term persistence. The two-state conditioned DRIP model was shown to reproduce the annual variability observed to a greater degree than the single state DRIP. / PhD Doctorate

Livestock grazing impacts more land than any other use. Yet knowledge of grazing impacts on native fauna is scarce. This thesis takes a predictive approach to investigating the effects of livestock grazing on Australian woodland birds, employing some novel methodological approaches and experimental designs. These include methods of analysis to handle zero-inflated data and the application of Bayesian statistics to analyse predictions based on expert opinion. The experimental designs have enabled impacts of grazing to be separated from the frequently confounding effects of other disturbances, and to consider the effect of grazing on habitat condition in the context of different surrounding land uses. A distinguishing feature of many datasets is their tendency to contain a large proportion of zero values. It can be difficult to extract ecological relationships from these datasets if we do not consider how these zeros arose and how to model them. Recent developments in modelling zero-inflated data are tested with the aim of making such methods more accessible to mainstream ecology. Through practical examples, we demonstrate how not accounting for zero-inflation can reduce our ability to detect relationships in ecological data and at worst lead to incorrect inference. The impact of grazing on birds was first examined through the elicitation of a priori predictions from 20 Australian ecologists. This expert knowledge was then used to inform a statistical model using Bayesian methods. The addition of expert data through priors in our model strengthened results under at least one grazing level for all but one bird species examined. This study highlights that in fields where there is extensive expert knowledge, yet little published data, the use of expert information as priors for ecological models is a cost effective way of making more confident predictions about the effect of management on biodiversity. A second set of a priori predictions were formulated using a mechanistic approach. Habitat structure is a major determinant of bird species diversity and livestock grazing is one mechanism by which structure is altered. Using available information on the vegetation strata utilised by each species for foraging and the strata most affected by grazing, predictions of the impact of grazing on each bird species were formulated. We found that foraging height preference was a good predictor of species’ susceptibility to grazing. This approach is a starting point for more complex predictive models, and avoids the circularity of post hoc interpretation of impact data. The confounding of grazing with tree clearing was addressed by examining the impact of pastoral management on birds in sub-tropical grassy eucalypt woodland in Southeast Queensland, where land management practices have made it possible to disentangle these effects. Changes in bird species indices were recorded across woodland and riparian habitats with and without trees across three levels of grazing, replicated over space and time. Tree removal had a dramatic influence on 78% of the bird fauna. 65% of species responded significantly to changes in grazing level and the abundance of 42% of species varied significantly with habitat, level of clearing and grazing. The impact of grazing on birds was most severe in riparian habitat. Finally, the extent to which landscape context and local habitat characteristics influence bird assemblages of riparian habitats in grazed landscapes is addressed. Over 80% of bird species responded significantly to changes in local riparian habitat characteristics regardless of context, while over 50% of species were significantly influenced by landscape context. The influence of landscape context increased as the surrounding landuse became more intensive. These results suggest that it is not enough to conserve riparian habitats alone but conservation and restoration plans must consider landscape context. The ability to predict which bird species will be most affected by grazing will facilitate the transformation of this industry into one that is both profitable and ecologically sustainable. Results from this thesis suggest that any level of commercial grazing is detrimental to some woodland birds. Habitats with high levels of grazing support a species-poor bird assemblage dominated by birds that are increasing nationally. However, provided trees are not cleared and landscape context is not intensively used, a rich and abundant bird fauna can coexist with moderate levels of grazing, including iconic woodland birds which are declining elsewhere in Australia.

Bayesian modelling, in which our prior belief about the distribution on model parameters is updated by observed data, is a popular approach to statistical data analysis. However, writing specific inference algorithms for Bayesian models by hand is time-consuming and requires significant machine learning expertise. Probabilistic programming promises to make Bayesian modelling easier and more accessible by letting the user express a generative model as a short computer program (with random variables), leaving inference to the generic algorithm provided by the compiler of the given language. However, it is not easy to design a probabilistic programming language correctly and define the meaning of programs expressible in it. Moreover, the inference algorithms used by probabilistic programming systems usually lack formal correctness proofs and bugs have been found in some of them, which limits the confidence one can have in the results they return. In this work, we apply ideas from the areas of programming language theory and statistics to show that probabilistic programming can be a reliable tool for Bayesian inference. The first part of this dissertation concerns the design, semantics and type system of a new, substantially enhanced version of the Tabular language. Tabular is a schema-based probabilistic language, which means that instead of writing a full program, the user only has to annotate the columns of a schema with expressions generating corresponding values. By adopting this paradigm, Tabular aims to be user-friendly, but this unusual design also makes it harder to define the syntax and semantics correctly and reason about the language. We define the syntax of a version of Tabular extended with user-defined functions and pseudo-deterministic queries, design a dependent type system for this language and endow it with a precise semantics. We also extend Tabular with a concise formula notation for hierarchical linear regressions, define the type system of this extended language and show how to reduce it to pure Tabular. In the second part of this dissertation, we present the first correctness proof for a Metropolis-Hastings sampling algorithm for a higher-order probabilistic language. We define a measure-theoretic semantics of the language by means of an operationally-defined density function on program traces (sequences of random variables) and a map from traces to program outputs. We then show that the distribution of samples returned by our algorithm (a variant of “Trace MCMC” used by the Church language) matches the program semantics in the limit.

The development of flexible and interpretable statistical methods is necessary in order to provide appropriate risk assessment measures for extreme events and natural disasters. In this thesis, we address this challenge by contributing to the developing research field of Extreme-Value Theory. We initially study the performance of existing parametric and non-parametric estimators of extremal dependence for multivariate maxima. As the dimensionality increases, non-parametric estimators are more flexible than parametric methods but present some loss in efficiency that we quantify under various scenarios. We introduce a statistical tool which imposes the required shape constraints on non-parametric estimators in high dimensions, significantly improving their performance. Furthermore, by embedding the tree-based max-stable nested logistic distribution in the Bayesian framework, we develop a statistical algorithm that identifies the most likely tree structures representing the data's extremal dependence using the reversible jump Monte Carlo Markov Chain method. A mixture of these trees is then used for uncertainty assessment in prediction through Bayesian model averaging. The computational complexity of full likelihood inference is significantly decreased by deriving a recursive formula for the nested logistic model likelihood. The algorithm performance is verified through simulation experiments which also compare different likelihood procedures. Finally, we extend the nested logistic representation to the spatial framework in order to jointly model multivariate variables collected across a spatial region. This situation emerges often in environmental applications but is not often considered in the current literature. Simulation experiments show that the new class of multivariate max-stable processes is able to detect both the cross and inner spatial dependence of a number of extreme variables at a relatively low computational cost, thanks to its Bayesian hierarchical representation. These innovative methods and models are implemented to study the concentration maxima of various air pollutants and how these are related to extreme weather conditions for a number of sites in California, one of the most populated and polluted states of the US. As a result, we provide comprehensive measures of air quality that can be used by communities and policymakers worldwide to better assess and manage the health, environmental and financial impacts of air pollution extremes.

When examining objects using tactile senses, individuals often incorporate multiple sources of haptic sensory information to estimate the object’s properties. How do our brains integrate various cues to form a single percept of the object? Previous research has indicated that integration from cues across sensory modalities is optimally achieved by weighting each cue according to its variance, such that more reliable cues have more weight in determining the percept. To explore this question in the context of a within-modality haptic setting, we assessed participants’ perception of edges that cross the index, middle, and ring fingers of the right hand. We used a 2-interval forced choice (2IFC) task to measure the acuity of each digit individually, as well as the acuity of all three digits working together, by asking participants to distinguish the locations of two closely spaced plastic edges. In examining the data, we considered three perceptual models, an optimal (Bayesian) model, an unweighted average model, and a winner-take-all model. The results indicate that participants perceived sub-optimally, such that the acuity of the three digits together did not exceed that of the best individual digit. We further investigated our question by having participants unknowingly undergo a 2IFC cue conflict condition, where they thought they were touching a straight edge which was actually staggered and thus gave each digit a different positional cue. Our analyses indicate that participants did not undertake optimal cue combination but are inconclusive with respect to which suboptimal strategy they employed. / Thesis / Master of Science (MSc) / This thesis investigates the neural mechanisms behind tactile perception, specifically how the brain combines multiple sensory cues to construct a unified percept when interacting with objects through touch. Typically, optimal sensory integration involves assigning more weight to more reliable cues. Our research focused on tactile integration by examining participants’ ability to perceive the positions of edges crossing their index, middle, and ring fingers simultaneously. The results indicated that, contrary to predictions, participants exhibited various sub-optimal cue integration strategies. Their ability to perceive the combined positions of all three fingers was not superior to that of the best-performing individual finger. We also explored cue conflict situations, where the locations of the tactile cues were no longer from a straight edge, unbeknown to participants, and the results here reinforced the finding that participants did not consistently employ optimal cue combination strategies. This research offers valuable insights into how the brain processes tactile information.

Methods for uncertainty quantification (UQ) and mitigation in the electrical power system are very basic, Monte Carlo (MC) method and its meta methods are generally deployed in most applications, due to its simplicity and easy to be generalised. They are adequate for a traditional power system when the load is predictable, and generation is controllable. However, the large penetration of low carbon technologies, such as solar panels, electric vehicles, and energy storage, has necessitated the needs for more comprehensive approaches to uncertainty as these technologies introduce new sources of uncertainties with larger volume and diverse characteristics, understanding source and consequences of uncertainty becomes highly complex issues. Traditional methods assume that for a given system it has a unique uncertainty characteristic, hence deal with the uncertainty of the system as a single component in applications. However, this view is no longer applicable in the new context as it neglects the important underlying information associated with individual uncertainty components. Therefore, this thesis aims at: i) systematically developing UQ methodologies to identify, discriminate, and quantify different uncertainty components (forward UQ), and critically to model and trace the associated sources independently (inverse UQ) to deliver new uncertainty information, such as, how uncertainty components generated from its sources, how uncertainty components correlate with each other and how uncertainty components propagate through system aggregation; ii) applying the new uncertainty information to further improve a range of fundamental power system applications from Load Forecasting (LF) to Energy Management System (EMS).In the EMS application, the proposed forward UQ methods enable the development of a decentralised system that is able to tap into the new uncertainty information concerning the correlations between load pattern across individual households, the characteristics of uncertainty components and their propagation through aggregation. The decentralised EMS was able to achieve peak and uncertainty reduction by 18% and 45% accordingly at the grid level. In the LF application, this thesis developed inverse UQ through a deep learning model to directly build the connection between uncertainty components and its corresponding sources. For Load Forecasting on expectation (point LF) and probability (probabilistic LF) and witnessed 20%/12% performance improvement compared to the state-of-the-art, such as Support Vector Regression (SVR), Autoregressive Integrated Moving Average (ARIMA), and Multiple Linear Quantile Regression (MLQR).

Si la question de l'origine du langage reste d'un abord compliqué, celle de l'origine des formes du langage semble plus susceptible de se confronter à la démarche expérimentale. Malgré leur infinie variété, d'évidentes régularités y sont présentes~: les universaux du langage. Nous les étudions par des raisonnements plus généraux sur l'émergence du langage, notamment sur la recherche de précurseurs onto- et phylogénétiques. Nous abordons trois thèmes principaux~: la situation de communication parlée, les architectures cognitives des agents et l'émergence des universaux du langage dans des sociétés d'agents. Notre première contribution est un modèle conceptuel des agents communicants en interaction, issu de notre analyse bibliographique. Nous en proposons ensuite une formalisation mathématique Bayésienne~: le modèle d'un agent est une distribution de probabilités, et la production et la perception sont des inférences bayésiennes. Cela permet la comparaison formelle des différents courants théoriques en perception et en production de la parole. Enfin, nos simulations informatiques de société d'agents identifient les conditions qui favorisent l'apparition des universaux du langage. / If the origin of language is difficult to properly study, the origin of its forms appears to be accessible to the experimental method. Languages, despite their large variety, display obvious regularities, the linguistic universals. We study them through more general reasoning about language emergence, in particular in the search of its precursors, both in ontogeny and phylogeny. We study three main themes: the communication situation, the agent's cognitive architectures and the emergence of linguistic universals in agent societies. Our first contribution is a conceptual model of communicating agents in interaction, emanating from our bibliographic survey. We then cast it into the Bayesian mathematical formalism: an agent model is a probability distribution, and production and perception are defined by Bayesian inference. This allows a formal comparison of speech perception and production theoretical trends. Finally, computer simulations of agent societies help identify the conditions that favor the appearance of linguistic universals.

The monitoring and assessment of prawn populations in New South Wales (NSW), Australia, has been identified as a continuing research priority by both the fishing industry and the fisheries managers. This dissertation presents a series of dynamic population models developed to evaluate the status of the eastern king prawn (Melicertus plebejus) and eastern school prawn (Metapenaeus macleayi) populations within NSW and to analyse the relative performance of a number of alternative management strategies involving the three fisheries that target these species. Monthly commercial prawn catch and effort data from 1984 to 2006 were used to calibrate the stock assessment models. Where possible, the results of previous research were used to develop the structure of the model and to provide estimates of biological parameters. A process of increasing model complexity, including the addition of physical processes, such as river discharge events and economic considerations, was undertaken in an attempt to develop the most appropriate model for the analysis of management strategies. The first model presented was used to undertake a single-species assessment of the eastern king prawn stock and was based on a delay-difference population model with four different representations of recruitment. This model was calibrated to observations using the Bayesian sampling/importance re-sampling method and used to test the effect of significant changes in the future catch on the stock. The second model presented is a size-based metapopulation model which incorporated the dynamics of school prawns over three habitats, being harvested by three different fishing methods. This model was used to test the effect of alternative climate variability scenarios on the stock. The third model presented is a multi-species, multi-fishery bio-economic model. This model was used to examine the impact of nine alternative economic scenarios, incorporating various combinations of input costs and product prices. The results from the use of these models indicated that neither of the prawn population appeared to be over-exploited. The analyses also indicated that none of the alternative management strategies were found to stand-out enough to justify a move away from the current management strategy of input controls and spatio-temporal closures, even under a range of future scenarios including climate change and large movements in input costs and product prices.

The monitoring and assessment of prawn populations in New South Wales (NSW), Australia, has been identified as a continuing research priority by both the fishing industry and the fisheries managers. This dissertation presents a series of dynamic population models developed to evaluate the status of the eastern king prawn (Melicertus plebejus) and eastern school prawn (Metapenaeus macleayi) populations within NSW and to analyse the relative performance of a number of alternative management strategies involving the three fisheries that target these species. Monthly commercial prawn catch and effort data from 1984 to 2006 were used to calibrate the stock assessment models. Where possible, the results of previous research were used to develop the structure of the model and to provide estimates of biological parameters. A process of increasing model complexity, including the addition of physical processes, such as river discharge events and economic considerations, was undertaken in an attempt to develop the most appropriate model for the analysis of management strategies. The first model presented was used to undertake a single-species assessment of the eastern king prawn stock and was based on a delay-difference population model with four different representations of recruitment. This model was calibrated to observations using the Bayesian sampling/importance re-sampling method and used to test the effect of significant changes in the future catch on the stock. The second model presented is a size-based metapopulation model which incorporated the dynamics of school prawns over three habitats, being harvested by three different fishing methods. This model was used to test the effect of alternative climate variability scenarios on the stock. The third model presented is a multi-species, multi-fishery bio-economic model. This model was used to examine the impact of nine alternative economic scenarios, incorporating various combinations of input costs and product prices. The results from the use of these models indicated that neither of the prawn population appeared to be over-exploited. The analyses also indicated that none of the alternative management strategies were found to stand-out enough to justify a move away from the current management strategy of input controls and spatio-temporal closures, even under a range of future scenarios including climate change and large movements in input costs and product prices.