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The human desire to explain the world is the driving force behind our species' rich history of scientific and technological advancement. The ability of successive generations to build cumulatively on the scientific progress made by their ancestors rests on the ability of individual minds to rapidly assimilate the explanatory models developed by those who came before. But is this explanatory, model-based way of thinking limited to deliberate, conscious cognition, with the larger, unconscious portion of the workings of the mind dependent on simpler mechanisms of association and prediction, or is explanation a more fundamental drive? In this dissertation I explore theoretical, empirical and computational attempts to shed some light on this question. I first present a number of theoretical advantages that model-based learning has over its associative counterparts. I focus particularly on the inferential phenomenon of \emph{explaining away}, which is difficult to account for in a model-free system of learning. Next I review some recent empirical literature which helps to establish just what mechanisms of learning are available to human infants and adults, including a number of findings that suggest that there is more to learning than mere prediction. Among these are a number of experiments suggesting that explaining away occurs in a variety of cognitive domains. Having set the stage, I report a new set of experiments, one with infants and two with adults, along with a related computational model, which provide further evidence for unconscious explaining away, and hence for some for of model-based inference, in the domain of abstract, relational pattern-learning. In particular, I find that when learners are presented with a novel environment of tone sequences, the structure of their initial experience with that environment, and implicitly the model of the environment which best accounts for that experience, influences what kinds of abstract structure can easily be learned later. If indeed learners are able to construct explanatory models of particular domains of experience which are then used to learn the details of each domain, it may undermine claims by some philosophers and cognitive scientists that asymmetries in learning across domains constitutes evidence for an innately modular organization of the mind.

Imagine a world where computational simulations can be inverted as easily as running them forwards, where data can be used to refine models automatically, and where the only expertise one needs to carry out powerful statistical analysis is a basic proficiency in scientific coding. Creating such a world is the ambitious long-term aim of probabilistic programming. The bottleneck for improving the probabilistic models, or simulators, used throughout the quantitative sciences, is often not an ability to devise better models conceptually, but a lack of expertise, time, or resources to realize such innovations. Probabilistic programming systems (PPSs) help alleviate this bottleneck by providing an expressive and accessible modeling framework, then automating the required computation to draw inferences from the model, for example finding the model parameters likely to give rise to a certain output. By decoupling model specification and inference, PPSs streamline the process of developing and drawing inferences from new models, while opening up powerful statistical methods to non-experts. Many systems further provide the flexibility to write new and exciting models which would be hard, or even impossible, to convey using conventional statistical frameworks. The central goal of this thesis is to improve and extend PPSs. In particular, we will make advancements to the underlying inference engines and increase the range of problems which can be tackled. For example, we will extend PPSs to a mixed inference-optimization framework, thereby providing automation of tasks such as model learning and engineering design. Meanwhile, we make inroads into constructing systems for automating adaptive sequential design problems, providing potential applications across the sciences. Furthermore, the contributions of the work reach far beyond probabilistic programming, as achieving our goal will require us to make advancements in a number of related fields such as particle Markov chain Monte Carlo methods, Bayesian optimization, and Monte Carlo fundamentals.

<p>The effects of climate change on forest dynamics will be determined by tree responses at different life-stages and different scales -- from establishment to maturity and from individuals to populations. Studies incorporating local factors, such as natural enemies, competition, or tree physiology, with sufficient variation in climate are lacking. The importance of global and regional climate variation vs. local conditions and responses is poorly understood and may only be addressed with large datasets capturing sufficient environmental variation. This dissertation uses several large datasets to examine tree demographic and ecophysiological responses to light, moisture, predation, and climate in eastern temperate forests of North Carolina. </p><p> First, I use a 19-yr seed rain record from 13 forest plots in the piedmont, transition zone, and mountains to examine how climate-mediated seed maturation and density-dependent seed predation processes increase population reproductive variation in nine temperate tree species (Chapter 1). I address several hypotheses explaining interannual reproductive variation, such as resource matching, predator satiation, and pulse resource dynamics. My results indicate that (1) interannual reproductive variation increased as a result of seed maturation and seed predation processes, (2) seed maturation rates increased under warm, wet conditions, and (3) seed predation rates exhibited negative and positive density-dependence, depending of tree species and type of seed predator (specialist insects vs. generalist vertebrates). Because positive density-dependent seed predation dampened and negative density-dependent seed predation amplified the effects of climate-mediated maturation on reproductive variation, this study showed evaluations of tree reproduction need to incorporate both climate and seed predation.</p><p> Next, I use an 11-yr record of annual tree seedling growth and survival in 20 tree species planted in the piedmont and mountains to quantify individual tree seedling growth and survival responses to spatial variation in resources and temporal variation in climate (Chapter 2). First, I tested whether height-mediated growth provides an advantage to large individuals in all environments by amplifying responses to light and moisture or only when those resources were plentiful. Second, I tested whether allometric and survival responses differed among species based on life-history strategies. Individual height amplified tree seedling growth. However, some species exhibited amplification at moderate to high resource levels as well as depression of growth in large individuals growing in low light and moisture environments. Shade intolerant species exhibited an increasing ratio of height to diameter growth and increasing survival probability with both increasing light and moisture resources. Conversely, shade tolerant species exhibited decreasing height to diameter ratio with increasing light, possibly because of biomass allocation toward acquisition of limiting light resources. Despite relative small effects of drought and winter temperature of tree seedling demography, the results of this study indicate that individual tree seedlings sensitive to light and moisture environments, such as large seedlings and seedlings of shade intolerant species, growing in shaded or xeric sites may be particularly vulnerable to climate induced mortality. </p><p> Finally, I examine interannual and interspecific variation in canopy conductance using four years of environmental (vapor pressure deficit, above canopy light, and soil moisture) and stem sap flux data from heat dissipation probes for six co-occurring tree species. I developed a state-space modeling framework for predicting canopy conductance and transpiration which incorporates uncertainty in canopy and observation uncertainty. This approach is used to evaluate the degree to which co-occur deciduous tree species exhibited drought tolerating and drought avoiding canopy responses and whether these patterns were maintained in the face of interannual variation in environmental drivers. Comparisons of canopy conductance responses to environmental forcing across species and years highlighted the importance of tree sensitivity to moisture limitation, both in terms of high vapor pressure deficit and low soil moisture, and tree hydraulic characteristics within diverse forest communities. The state-space model produced similar parameter estimates to the more traditional boundary line analysis, performed well in terms of in-sample and out-of-sample prediction of sap flux observations, and provided for coherent incorporation of parameter, process, and observation errors in predicting missing data (i.e., gap-filling), canopy conductance, and transpiration.</p><p> Much needs to be learned about forest community responses to climate change, however these responses depend on local growing conditions (light and moisture), the life-stage being examined (seedlings, juveniles, or mature trees), and the scale of inference (individuals, canopies, or populations). Because climate change will not occur in isolation from other factors, such as stand age or disturbance, studies must characterize tree responses across multidimensional gradients in growing conditions. This dissertation addresses these challenges using large demographic and ecophysiological datasets well-suited for global change research.</p> / Dissertation

People talk about coherent episodes of their experience, leading to strong dependencies between words and the contexts in which they appear. Consequently, language within a context is more repetitive and more coherent than language sampled from across contexts. In this dissertation, I investigated how patterns in infant-directed speech differ under context-sensitive compared to context-independent analysis. In particular, I tested the hypothesis that cues to word boundaries may be clearer within contexts. Analyzing a large corpus of transcribed infant-directed speech, I implemented three different approaches to defining context: a top-down approach using the occurrence of key words from pre-determined context lists, a bottom-up approach using topic modeling, and a subjective coding approach where contexts were determined by open-ended, subjective judgments of coders reading sections of the transcripts. I found substantial agreement among the context codes from the three different approaches, but also important differences in the proportion of the corpus that was identified by context, the distribution of the contexts identified, and some characteristics of the utterances selected by each approach. I discuss implications for the use and interpretation of contexts defined in each of these three ways, and the value of a multiple-method approach in the exploration of context. To test the strength of statistical cues to word boundaries in context-specific sub-corpora relative to a context-independent analysis of cues to word boundaries, I used a resampling procedure to compare the segmentability of context sub-corpora defined by each of the three approaches to a distribution of random sub-corpora, matched for size for each context sub-corpus. Although my analyses confirmed that context-specific sub-corpora are indeed more repetitive, the data did not support the hypothesis that speech within contexts provides richer information about the statistical dependencies among phonemes than is available when analyzing the same statistical dependencies without respect to context. Alternative hypotheses and future directions to further elucidate this phenomenon are discussed. / 2019-02-17

Recent studies indicate a relationship between measures of urban form as applied to urban and suburban areas, and obesity, a risk factor for heart disease. Measures of urban form for exurban and rural areas are considerably scarce; such measures could prove useful in measuring relationships between urban form and both mortality and morbidity in such areas. In modeling area-level mortality, geographic relationships between counties warrant consideration because geographically adjacent areas tend to have more in common than areas farther from each other. We modify county-level indices of urban form found in the literature so that they can be applied to exurban and rural counties. We then use these indices in a Bayesian spatial model that accounts for spatial autocorrelation to determine if there is a relationship between such measures and cardiovascular disease mortality for white males age 35 and older for the time period 1999-2001. Issues related to the formation and usefulness of the indices, and issues related to the spatial model, are discussed. Maps of observed and expected relative risk of mortality are presented.

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In Wilderness Search and Rescue (WiSAR), the incident commander (IC) creates a probability distribution map of the likely location of the missing person. This map is important because it guides the IC in allocating search resources and coordinating efforts, but it often depends almost exclusively on prior experience and subjective judgment. We propose a Bayesian model that utilizes publicly available terrain features data to help model lost-person behaviors. This approach enables domain experts to encode uncertainty in their prior estimations and also make it possible to incorporate human-behavior data collected in the form of posterior distributions, which are used to build a first-order Markov transition matrix for generating a temporal, posterior predictive probability distribution map. The map can work as a base to be augmented by search and rescue workers to incorporate additional information. Using a Bayes Chi-squared test for goodness-of-fit, we show that the model fits a synthetic dataset well. This model also serves as a foundation of a larger framework that allows for easy expansion to incorporate additional factors such as season and weather conditions that affect the lost-person's behaviors. Once a probability distribution map is in place, areas with higher probabilities are searched first in order to find the missing person in the shortest expected time. When using a Unmanned Aerial Vehicle (UAV) to support search, the onboard video camera should cover as much of the important areas as possible within a set time. We explore several algorithms (with and without set destination) and describe some novel techniques in solving this path-planning problem and compare their performances against typical WiSAR scenarios. This problem is NP-hard, but our algorithms yield high quality solutions that approximate the optimal solution, making efficient use of the limited UAV flying time. The capability of planning a path with a set destination also enables the UAV operator to plan a path strategically while letting the UAV plan the path locally.

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Estudos recentes relacionados ao meio ambiente vêm ganhando grande destaque em todo o mundo devido ao fato dos níveis de poluição e a destruição das reservas naturais terem aumentado de maneira alarmante nos últimos anos. As grandes cidades são as que mais sofrem com a poluição e aqui serão estudados os níveis de poluição do ar em duas cidades em particular, a Cidade do México e São Paulo. A Cidade do México apresenta sérios problemas com os níveis de ozônio e São Paulo é a cidade brasileira com os maiores problemas relacionados à poluição. Entre os diferentes modelos considerados para analisar dados de poluição do ar, pode-se considerar o uso de modelos de séries temporais para modelar as médias diárias ou semanais de poluição. Nessa direção pode-se usar modelos de volatilidade estocástica. Essa família de modelos estatísticos tem sido extensivamente usada para analisar séries temporais financeiras, porém não se observa muitas aplicações em dados ambientais e de saúde. Modelos de volatilidade estocástica bivariados e multivariados, sob a aproximação Bayesiana, foram considerados para analisar os dados, especialmente usando métodos MCMC (Monte Carlo em Cadeias de Markov) para obter os sumários a posteriori de interesse, pois pode-se ter muitas dificuldades usando métodos clássicos de inferência estatística / Recent studies related to environmental has been considered in all world due to increasing levels of pollution and of natural resources destruction especially, in the last years. The largest cities in the world are the ones been mostly affected by pollution and in this work we consider the analysis of air pollution data of two important cities: Mexico City and São Paulo. The Mexico City presents serious problems of ozone levels and São Paulo is the Brazilian city with the largest problems related to air pollution. Among the different models which could be used to analyze air pollution data, we consider the use of time series modeling to the weekly or daily levels of pollution. In this way, we consider the use of volatility stochastic models. This family of models has been well explored with financial data but not well explored to analyze environmental and health data. Bivariate and multivariate stochastic models under the Bayesian approach were considered to analyze the data, especially using MCMC (Markov Chain Monte Carlo) methods to obtain the posterior summary of interest, since we usually have big difficulties using standard classical inference methods