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Fossil Agamidae from Western Australia have been the subject of limited study. To aid in fossil agamid identification, Hocknull (2002) examined the maxilla and dentary of several extant species from Australia and determined diagnostic characters for various species groups. In the study here, fossil agamids from two localities in Western Australia, Hastings Cave and Horseshoe Cave, were examined, grouped, and identified to the lowest unambiguous taxonomic level. Morphometric analyses were conducted to compare morphotypes, and find additional diagnostic characters. From Hastings Cave there were two maxilla morphotypes and three dentary morphotypes. Based on identifications, taxa present at this locality were Pogona and Ctenophorus. Horseshoe Cave contained three maxilla morphotypes and two dentary morphotypes; taxa present were Pogona, Tympanocryptis, and Ctenophorus. Morphometric analyses showed separation between groups; however, the dentary morphotype separation was not as clear. Each morphotype identification matched a species in the respective localities today, but identifications are cautious.

L'étude de la démographie dans une perspective historique participe à la compréhension des processus évolutifs. Les données de diversité génétique sont potentiellement informatives quant au passé démographique des populations: en effet, ce passé est enregistré avec perte d'information par les marqueurs moléculaires, par l'intermédiaire de leur histoire généalogique et mutationnelle. L'acquisition de données de diversité génétique est de plus en plus rapide et aisée, et concerne potentiellement n'importe quel organisme d'intérêt. D'où un effort dans la dernière décennie pour développer les outils statistiques permettant d'extraire l'information démographique des données de typage génétique.<br />La présente thèse propose une extension de la méthode d'inférence bayésienne développée en 1999 par M. Beaumont. Comme la méthode originale, (i) elle est basée sur le coalescent de Kingman avec variations d'effectif, (ii) elle utilise l'algorithme de Metropolis-Hastings pour échantillonner selon la loi a posteriori des paramètres d'intérêt et (iii) elle permet de traiter des données de typage à un ou plusieurs microsatellites indépendants. La version étendue généralise les modèles démographique et mutationnel supposés dans la méthode initiale: elle permet d'inférer les paramètres d'un modèle de fondation-explosion pour la population échantillonnée et d'un modèle mutationnel à deux phases, pour les marqueurs microsatellites typés. C'est la première fois qu'une méthode probabiliste exacte incorpore pour les microsatellites un modèle mutationnel autorisant des sauts.<br />Le modèle démographique et mutationnel est exploré. L'analyse de jeux de données simulés permet d'illustrer et de comparer la loi a posteriori des paramètres pour des scénarios historiques: par exemple une stabilité démographique, une croissance exponentielle et une fondation-explosion. Une typologie des lois a posteriori est proposée. Des recommandations sur l'effort de typage dans les études empiriques sont données: un unique marqueur microsatellite peut conduire à une loi a posteriori très structurée. Toutefois, les zones de forte densité a posteriori représentent des scénarios de différents types. 50 génomes haploides typés à 5 marqueurs microsatellites suffisent en revanche à détecter avec certitude (99% de la probabilité a posteriori) une histoire de fondation-explosion tranchée. Les conséquences de la violation des hypothèses du modèle démographique sont discutées, ainsi que les interactions entre processus et modèle mutationnel. En particulier, il est établi que le fait de supposer un processus mutationnel conforme au modèle SMM, alors que ce processus est de type TPM, peut générer un faux signal de déséquilibre génétique. La modélisation des sauts mutationnels permet de supprimer ce faux signal.<br />La méthode est succinctement appliquée à l'étude de deux histoires de fondation-explosion: l'introduction du chat Felis catus sur les îles Kerguelen et celle du surmulot Rattus norvegicus sur les îles du large de la Bretagne. Il est d'abord montré que la méthode fréquentiste développée par Cornuet et Luikart (1996) ne permet pas de détecter les fondations récentes et drastiques qu'ont connu ces populations. Cela est vraisemblablement dû à des effets contraires de la fondation et de l'explosion, sur les statistiques utilisées dans cette méthode.<br />La méthode bayésienne ne détecte pas non plus la fondation si l'on force une histoire démographique en marche d'escalier, pour la même raison. La fondation et l'explosion deviennent détectables si le modèle démographique les autorise. Toutefois, les dépendances entre les paramètres du modèle empêchent de les inférer marginalement avec précision. Toute information a priori sur un paramètre contraint fortement les valeurs des autres paramètres. Ce constat confirme le potentiel de populations d'histoire documentée pour l'estimation indirecte des paramètres d'un modèle de mutation des marqueurs.

This thesis contains four papers and two introductory chapters. It is mainly devoted to problems concerning random growth models related to the Loewner differential equation. In Paper I we derive a rate of convergence of the Loewner driving function for loop-erased random walk to Brownian motion with speed 2 on the unit circle, the Loewner driving function for radial SLE(2). Thereby we provide the first instance of a formal derivation of a rate of convergence for any of the discrete models known to converge to SLE. In Paper II we use the known convergence of (radial) loop-erased random walk to radial SLE(2) to prove that the scaling limit of loop-erased random walk excursion in the upper half plane is chordal SLE(2). Our proof relies on a version of Wilson’s algorithm for weighted graphs together with a Beurling-type hitting estimate for random walk excursion. We also establish and use the convergence of the radial SLE path to the chordal SLE path as the bulk point tends to a boundary point. In the final section we sketch how to extend our results to more general domains. In Paper III we prove an upper bound on the optimal Hölder exponent for the chordal SLE path parameterized by capacity and thereby establish the optimal exponent as conjectured by J. Lind. We also give a new proof of the lower bound. Our proofs are based on sharp estimates of moments of the derivative of the inverse SLE map. In particular, we improve an estimate of G. F. Lawler. In Paper IV we consider radial Loewner evolutions driven by unimodular Lévy processes. We rescale the hulls of the evolution by capacity, and prove that the weak limit of the rescaled hulls exists. We then study a random growth model obtained by driving the Loewner equation with a compound Poisson process with two real parameters: the intensity of the underlying Poisson process and a localization parameter of the Poisson kernel which determines the jumps. A particular choice of parameters yields a growth process similar to the Hastings-Levitov HL(0) model. We describe the asymptotic behavior of the hulls with respect to the parameters, showing that growth tends to become localized as the jump parameter increases. We obtain deterministic evolutions in one limiting case, and Loewner evolution driven by a unimodular Cauchy process in another. We also show that the Hausdorff dimension of the limiting rescaled hulls is equal to 1.

Thesis (Ph. D.)--University of Chicago, Dept. of History, August 2001. / Includes bibliographical references. Also available on the Internet.

Many prior distributions are suggested for variance parameters in the hierarchical model. The “Non-informative” interval of the conjugate inverse-gamma prior might cause problems. I consider three priors – conjugate inverse-gamma, log-normal and truncated normal for the variance parameters and do the numerical analysis on Gelman’s 8-schools data. Then with the posterior draws, I compare the Bayesian credible intervals of parameters using the three priors. I use predictive distributions to do predictions and then discuss the differences of the three priors suggested. / text

This work considers black-box Bayesian inference over high-dimensional parameter spaces. The well-known and widely respected adaptive Metropolis (AM) algorithm is extended herein to asymptotically scale uniformly with respect to the underlying parameter dimension, by respecting the variance, for Gaussian targets. The result- ing algorithm, referred to as the dimension-independent adaptive Metropolis (DIAM) algorithm, also shows improved performance with respect to adaptive Metropolis on non-Gaussian targets. This algorithm is further improved, and the possibility of probing high-dimensional targets is enabled, via GPU-accelerated numerical libraries and periodically synchronized concurrent chains (justified a posteriori). Asymptoti- cally in dimension, this massively parallel dimension-independent adaptive Metropolis (MPDIAM) GPU implementation exhibits a factor of four improvement versus the CPU-based Intel MKL version alone, which is itself already a factor of three improve- ment versus the serial version. The scaling to multiple CPUs and GPUs exhibits a form of strong scaling in terms of the time necessary to reach a certain convergence criterion, through a combination of longer time per sample batch (weak scaling) and yet fewer necessary samples to convergence. This is illustrated by e ciently sampling from several Gaussian and non-Gaussian targets for dimension d 1000.

Markov chain Monte Carlo (MCMC) methods are a widely applicable class of algorithms for estimating integrals in statistical inference problems. A common approach in MCMC methods is to introduce additional auxiliary variables into the Markov chain state and perform transitions in the joint space of target and auxiliary variables. In this thesis we consider novel methods for using auxiliary variables within MCMC methods to allow approximate inference in otherwise intractable models and to improve sampling performance in models exhibiting challenging properties such as multimodality. We first consider the pseudo-marginal framework. This extends the Metropolis–Hastings algorithm to cases where we only have access to an unbiased estimator of the density of target distribution. The resulting chains can sometimes show ‘sticking’ behaviour where long series of proposed updates are rejected. Further the algorithms can be difficult to tune and it is not immediately clear how to generalise the approach to alternative transition operators. We show that if the auxiliary variables used in the density estimator are included in the chain state it is possible to use new transition operators such as those based on slice-sampling algorithms within a pseudo-marginal setting. This auxiliary pseudo-marginal approach leads to easier to tune methods and is often able to improve sampling efficiency over existing approaches. As a second contribution we consider inference in probabilistic models defined via a generative process with the probability density of the outputs of this process only implicitly defined. The approximate Bayesian computation (ABC) framework allows inference in such models when conditioning on the values of observed model variables by making the approximation that generated observed variables are ‘close’ rather than exactly equal to observed data. Although making the inference problem more tractable, the approximation error introduced in ABC methods can be difficult to quantify and standard algorithms tend to perform poorly when conditioning on high dimensional observations. This often requires further approximation by reducing the observations to lower dimensional summary statistics. We show how including all of the random variables used in generating model outputs as auxiliary variables in a Markov chain state can allow the use of more efficient and robust MCMC methods such as slice sampling and Hamiltonian Monte Carlo (HMC) within an ABC framework. In some cases this can allow inference when conditioning on the full set of observed values when standard ABC methods require reduction to lower dimensional summaries for tractability. Further we introduce a novel constrained HMC method for performing inference in a restricted class of differentiable generative models which allows conditioning the generated observed variables to be arbitrarily close to observed data while maintaining computational tractability. As a final topicwe consider the use of an auxiliary temperature variable in MCMC methods to improve exploration of multimodal target densities and allow estimation of normalising constants. Existing approaches such as simulated tempering and annealed importance sampling use temperature variables which take on only a discrete set of values. The performance of these methods can be sensitive to the number and spacing of the temperature values used, and the discrete nature of the temperature variable prevents the use of gradient-based methods such as HMC to update the temperature alongside the target variables. We introduce new MCMC methods which instead use a continuous temperature variable. This both removes the need to tune the choice of discrete temperature values and allows the temperature variable to be updated jointly with the target variables within a HMC method.

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.

Esta tese tem como objetivo principal a proposição de solução para o problema do encaixe de moldes em tecidos listrados da indústria do vestuário. Os moldes são peças com formato irregular que devem ser dispostos sobre a matéria-prima, neste caso o tecido, para a etapa posterior de corte. No problema específico do encaixe em tecidos listrados, o local em que os moldes são posicionados no tecido deve garantir que, após a confecção da peça, as listras apresentem continuidade. Assim, a fundamentação teórica do trabalho abrange temas relacionados à moda e ao design do vestuário, como os tipos e padronagens de tecidos listrados, e as possibilidades de rotação e colocação dos moldes sobre tecidos listrados. Na fundamentação teórica também são abordados temas da pesquisa em otimização combinatória como: características dos problemas bidimensionais de corte e encaixe e algoritmos utilizados por diversos autores para solucionar o problema. Ainda na parte final da fundamentação teórica são descritos o método Cadeia de Markov Monte Carlo e o algoritmo de Metropolis-Hastings. Com base na pesquisa bibliográfica, foram propostos dois algoritmos distintos para lidar com o problema de encaixe de moldes em tecidos listrados: algoritmo com pré-processamento e algoritmo de busca do melhor encaixe utilizando o algoritmo de Metropolis-Hastings. Ambos foram implementados no software Riscare Listrado, que é uma continuidade do software Riscare para tecidos lisos desenvolvido em Alves (2010). Para testar o desempenho dos dois algoritmos foram utilizados seis problemas benchmarks da literatura e proposto um novo problema denominado de camisa masculina. Os problemas benchmarks da literatura foram propostos para matéria-prima lisa e o problema camisa masculina especificamente para tecidos listrados. Entre os dois algoritmos desenvolvidos, o algoritmo de busca do melhor encaixe apresentou resultados com melhores eficiências de utilização do tecido para todos os problemas propostos. Quando comparado aos melhores resultados publicados na literatura para matéria-prima lisa, o algoritmo de busca do melhor encaixe apresentou encaixes com eficiências inferiores, porém com resultados superiores ao recomendado pela literatura específica da área de moda para tecidos estampados. / This thesis proposes the solution for the packing problem of patterns on striped fabric in clothing industry. The patterns are pieces with irregular form that should be placed on raw material which is, in this case, the fabric. This fabric is cut after packing. In the specific problem of packing on striped fabric, the position that patterns are put in the fabric should ensure that, after the clothing sewing, the stripes should present continuity. Thus, the theoretical foundation of this project includes subjects about fashion and clothing design, such as types and rapports of striped fabric, and the possibilities of rotation and the correct place to put the patterns on striped fabric. In the theoretical foundation, there are also subjects about research in combinatorial optimization as: characteristics about bi-dimensional packing and cutting problems and algorithms used for several authors to solve the problem. In addition, the Markov Chain Monte Carlo method and the Metropolis-Hastings algorithm are described at end of theoretical foundation. Based on the bibliographic research, two different algorithms for the packing problem with striped fabric are proposed: algorithm with pre-processing step and algorithm of searching the best packing using the Metropolis-Hastings algorithm. Both algorithms are implemented in the Striped Riscare software, which is a continuity of Riscare software for clear fabrics developed in the Masters degree of the author. Both algorithms performances are tested with six literature benchmark problems and a new problem called “male shirt” is proposed here. The benchmark problems of literature were iniatially proposed for clear raw material and the male shirt problem, specifically for striped fabrics. Between the two developed algorithms, the algorithm of searching the best packing has shown better results with better efficiencies of the fabric usage for all the problems tested. When compared to the best results published in the literature for clear raw material, the algorithm of searching the best packing has shown packings with lower efficiencies. However, it showed results higher than recommended for the specific literature of fashion design for patterned fabrics.