Bayesian synthetic likelihood

Having the ability to work with complex models can be highly beneficial, but the computational cost of doing so is often large. Complex models often have intractable likelihoods, so methods that directly use the likelihood function are infeasible. In these situations, the benefits of working with likelihood-free methods become apparent. Likelihood-free methods, such as parametric Bayesian indirect likelihood that uses the likelihood of an alternative parametric auxiliary model, have been explored throughout the literature as a good alternative when the model of interest is complex. One of these methods is called the synthetic likelihood (SL), which assumes a multivariate normal approximation to the likelihood of a summary statistic of interest. This paper explores the accuracy and computational efficiency of the Bayesian version of the synthetic likelihood (BSL) approach in comparison to a competitor known as approximate Bayesian computation (ABC) and its sensitivity to its tuning parameters and assumptions. We relate BSL to pseudo-marginal methods and propose to use an alternative SL that uses an unbiased estimator of the exact working normal likelihood when the summary statistic has a multivariate normal distribution. Several applications of varying complexity are considered to illustrate the findings of this paper.

Bayesian fisheries stock assessment: integrating and updating knowledge

In this thesis the use of the Bayesian approach to statistical inference in fisheries stock assessment is studied. The work was conducted in collaboration of the Finnish Game and Fisheries Research Institute by using the problem of monitoring and prediction of the juvenile salmon population in the River Tornionjoki as an example application. The River Tornionjoki is the largest salmon river flowing into the Baltic Sea. This thesis tackles the issues of model formulation and model checking as well as computational problems related to Bayesian modelling in the context of fisheries stock assessment. Each article of the thesis provides a novel method either for extracting information from data obtained via a particular type of sampling system or for integrating the information about the fish stock from multiple sources in terms of a population dynamics model. Mark-recapture and removal sampling schemes and a random catch sampling method are covered for the estimation of the population size. In addition, a method for estimating the stock composition of a salmon catch based on DNA samples is also presented. For most of the articles, Markov chain Monte Carlo (MCMC) simulation has been used as a tool to approximate the posterior distribution. Problems arising from the sampling method are also briefly discussed and potential solutions for these problems are proposed. Special emphasis in the discussion is given to the philosophical foundation of the Bayesian approach in the context of fisheries stock assessment. It is argued that the role of subjective prior knowledge needed in practically all parts of a Bayesian model should be recognized and consequently fully utilised in the process of model formulation.

Discovering hidden structures in molecular data using a Bayesian partition model approach

Advancements in the analysis techniques have led to a rapid accumulation of biological data in databases. Such data often are in the form of sequences of observations, examples including DNA sequences and amino acid sequences of proteins. The scale and quality of the data give promises of answering various biologically relevant questions in more detail than what has been possible before. For example, one may wish to identify areas in an amino acid sequence, which are important for the function of the corresponding protein, or investigate how characteristics on the level of DNA sequence affect the adaptation of a bacterial species to its environment. Many of the interesting questions are intimately associated with the understanding of the evolutionary relationships among the items under consideration. The aim of this work is to develop novel statistical models and computational techniques to meet with the challenge of deriving meaning from the increasing amounts of data. Our main concern is on modeling the evolutionary relationships based on the observed molecular data. We operate within a Bayesian statistical framework, which allows a probabilistic quantification of the uncertainties related to a particular solution. As the basis of our modeling approach we utilize a partition model, which is used to describe the structure of data by appropriately dividing the data items into clusters of related items. Generalizations and modifications of the partition model are developed and applied to various problems. Large-scale data sets provide also a computational challenge. The models used to describe the data must be realistic enough to capture the essential features of the current modeling task but, at the same time, simple enough to make it possible to carry out the inference in practice. The partition model fulfills these two requirements. The problem-specific features can be taken into account by modifying the prior probability distributions of the model parameters. The computational efficiency stems from the ability to integrate out the parameters of the partition model analytically, which enables the use of efficient stochastic search algorithms.

Diagnostic Molecular Markers for Phosphine Resistance in U.S. Populations of Tribolium castaneum and Rhyzopertha dominica

Stored product beetles that are resistant to the fumigant pesticide phosphine (hydrogen phosphide) gas have been reported for more than 40 years in many places worldwide. Traditionally, determination of phosphine resistance in stored product beetles is based on a discriminating dose bioassay that can take up to two weeks to evaluate. We developed a diagnostic cleaved amplified polymorphic sequence method, CAPS, to detect individuals with alleles for strong resistance to phosphine in populations of the red flour beetle, Tribolium castaneum, and the lesser grain borer, Rhyzopertha dominica, according to a single nucleotide mutation in the dihydrolipoamide dehydrogenase (DLD) gene. We initially isolated and sequenced the DLD genes from susceptible and strongly resistant populations of both species. The corresponding amino acid sequences were then deduced. A single amino acid mutation in DLD in populations of T.castaneum and R.dominica with strong resistance was identified as P45S in T.castaneum and P49S in R.dominica, both collected from northern Oklahoma, USA. PCR products containing these mutations were digested by the restriction enzymes MboI and BstNI, which revealed presence or absence, respectively of the resistant (R) allele and allowed inference of genotypes with that allele. Seven populations of T.castaneum from Kansas were subjected to discriminating dose bioassays for the weak and strong resistance phenotypes. Application of CAPS to these seven populations confirmed the R allele was in high frequency in the strongly resistant populations, and was absent or at a lower frequency in populations with weak resistance, which suggests that these populations with a low frequency of the R allele have the potential for selection of the strong resistance phenotype. CAPS markers for strong phosphine resistance will help to detect and confirm resistant beetles and can facilitate resistance management actions against a given pest population.

Atypical correlation of otolith strontium : calcium and barium : calcium across a marine–freshwater life history transition of a diadromous fish

Variation in strontium (Sr) and barium (Ba) within otoliths is invaluable to studies of fish diadromy. Typically, otolith Sr : Ca is positively related to salinity, and the ratios of Ba and Sr to calcium (Ca) vary in opposite directions in relation to salinity. In this study of jungle perch, Kuhlia rupestris, otolith Sr : Ca and Ba : Ca, however, showed the same rapid increase as late-larval stages transitioned directly from a marine to freshwater environment. This transition was indicated by a microstructural check mark on otoliths at 35–45 days age. As expected ambient Sr was lower in the fresh than the marine water, however, low Ca levels (0.4 mg L–1) of the freshwater resulted in the Sr : Ca being substantially higher than the marine water. Importantly, the otolith Sr : Ba ratio showed the expected pattern of a decrease from the marine to freshwater stage, illustrating that Sr : Ba provided a more reliable inference of diadromous behaviour based on prior expectations of their relationship to salinity, than did Sr : Ca. The results demonstrate that Ca variation in freshwaters can potentially be an important influence on otolith element : Ca ratios and that inferences of marine–freshwater habitat use from otolith Sr : Ca alone can be problematic without an understanding of water chemistry.

Joint Regression and Association Models for Repeated Categorical Responses

The focus of this study is on statistical analysis of categorical responses, where the response values are dependent of each other. The most typical example of this kind of dependence is when repeated responses have been obtained from the same study unit. For example, in Paper I, the response of interest is the pneumococcal nasopharengyal carriage (yes/no) on 329 children. For each child, the carriage is measured nine times during the first 18 months of life, and thus repeated respones on each child cannot be assumed independent of each other. In the case of the above example, the interest typically lies in the carriage prevalence, and whether different risk factors affect the prevalence. Regression analysis is the established method for studying the effects of risk factors. In order to make correct inferences from the regression model, the associations between repeated responses need to be taken into account. The analysis of repeated categorical responses typically focus on regression modelling. However, further insights can also be gained by investigating the structure of the association. The central theme in this study is on the development of joint regression and association models. The analysis of repeated, or otherwise clustered, categorical responses is computationally difficult. Likelihood-based inference is often feasible only when the number of repeated responses for each study unit is small. In Paper IV, an algorithm is presented, which substantially facilitates maximum likelihood fitting, especially when the number of repeated responses increase. In addition, a notable result arising from this work is the freely available software for likelihood-based estimation of clustered categorical responses.

Bayesian statistical analysis of bacterial diversity

Bacteria play an important role in many ecological systems. The molecular characterization of bacteria using either cultivation-dependent or cultivation-independent methods reveals the large scale of bacterial diversity in natural communities, and the vastness of subpopulations within a species or genus. Understanding how bacterial diversity varies across different environments and also within populations should provide insights into many important questions of bacterial evolution and population dynamics. This thesis presents novel statistical methods for analyzing bacterial diversity using widely employed molecular fingerprinting techniques. The first objective of this thesis was to develop Bayesian clustering models to identify bacterial population structures. Bacterial isolates were identified using multilous sequence typing (MLST), and Bayesian clustering models were used to explore the evolutionary relationships among isolates. Our method involves the inference of genetic population structures via an unsupervised clustering framework where the dependence between loci is represented using graphical models. The population dynamics that generate such a population stratification were investigated using a stochastic model, in which homologous recombination between subpopulations can be quantified within a gene flow network. The second part of the thesis focuses on cluster analysis of community compositional data produced by two different cultivation-independent analyses: terminal restriction fragment length polymorphism (T-RFLP) analysis, and fatty acid methyl ester (FAME) analysis. The cluster analysis aims to group bacterial communities that are similar in composition, which is an important step for understanding the overall influences of environmental and ecological perturbations on bacterial diversity. A common feature of T-RFLP and FAME data is zero-inflation, which indicates that the observation of a zero value is much more frequent than would be expected, for example, from a Poisson distribution in the discrete case, or a Gaussian distribution in the continuous case. We provided two strategies for modeling zero-inflation in the clustering framework, which were validated by both synthetic and empirical complex data sets. We show in the thesis that our model that takes into account dependencies between loci in MLST data can produce better clustering results than those methods which assume independent loci. Furthermore, computer algorithms that are efficient in analyzing large scale data were adopted for meeting the increasing computational need. Our method that detects homologous recombination in subpopulations may provide a theoretical criterion for defining bacterial species. The clustering of bacterial community data include T-RFLP and FAME provides an initial effort for discovering the evolutionary dynamics that structure and maintain bacterial diversity in the natural environment.

Computing the Stochastic Complexity of Simple Probabilistic Graphical Models

Minimum Description Length (MDL) is an information-theoretic principle that can be used for model selection and other statistical inference tasks. There are various ways to use the principle in practice. One theoretically valid way is to use the normalized maximum likelihood (NML) criterion. Due to computational difficulties, this approach has not been used very often. This thesis presents efficient floating-point algorithms that make it possible to compute the NML for multinomial, Naive Bayes and Bayesian forest models. None of the presented algorithms rely on asymptotic analysis and with the first two model classes we also discuss how to compute exact rational number solutions.

Nonideal negative resistors and capacitors using an operational amplifier

Negative impedance converters (NIC's) may be used to realize negative driving-point impedances. The effect of the nonideal characteristics of the operational amplifier such as finite frequencydependent gain and output impedance on the performance of the negative impedances is analyzed. Detailed equivalent circuits showing the additional positive or negative inductive impedances due to the nonideal characteristics are given for negative resistance and negative capacitance realizations, and their relative performances are compared. The experimental results confirm the validity of the equivalent circuits. The effect of the slew rate of the operational amplifier on the maximum signal-handling capability (SHC) of the negative impedances at high frequencies is studied. Practical design considerations for achieving wider bandwidth as well as improved SHC are discussed.

The Catholic Internet discussion on cloning : A study in cognitive rhetoric

This work combines the cognitive theory of folk-theoretical thought with the classical Aristotelian theory of artistic proof in rhetoric. The first half of the work discusses the common ground shared by the elements of artistic proof (logos, pathos, ethos) and the elements of folk-theoretical thought (naïve physics, folk biology, folk psychology, naïve sociology). Combining rhetoric with the cognitive theory of folk-theoretical thought creates a new point of view for argumentation analysis. The logos of an argument can be understood as the inferential relations established between the different parts of an argument. Consequently, within this study the analysis of logos is to be viewed as the analysis of the inferential folk-theoretical elements that make the suggested factual states-of-things appear plausible within given argumentative structures. The pathos of an argumentative structure can be understood as determining the quality of the argumentation in question in the sense that emotive elements play a great part in what can be called a distinction between good and deceptive rhetoric. In the context of this study the analysis of pathos is to be viewed as the analysis of the emotive content of argumentative structures and of whether they aim at facilitating surface- or deep cognitive elaboration of the suggested matters. The ethos of an argumentative structure means both the speaker-presentation and audience-construct that can be discerned within a body of argumentation. In the context of this study, the analysis of ethos is to be understood as the analysis of mutually manifest cognitive environments in the context of argumentation. The theory is used to analyse Catholic Internet discussion concerning cloning. The discussion is divided into six themes: Human Dignity, Sacred Family, Exploitation / Dehumanisation, Playing God, Monsters and Horror Scenarios and Ensoulment. Each theme is analysed for both the rhetorical and the cognitive elements that can be seen creating persuasive force within the argumentative structures presented. It is apparent that the Catholic voices on the Internet extensively oppose cloning. The voices utilise rhetoric that is aggressive and pejorative more often than not. Furthermore, deceptive rhetoric (in the sense presented above) plays a great part in argumentative structures of the Catholic voices. The theory of folk-theoretical thought can be seen as a useful tool for analysing the possible reasons why the Catholic speakers think about cloning and choose to present cloning in their argumentation as they do. The logos utilized in the argumentative structures presented can usually be viewed as based on folk-theoretical inference concerning biology and psychology. The structures of pathos utilized generally appear to aim at generating fear appeal in the assumed audiences, often incorporating counter-intuitive elements. The ethos utilised in the arguments generally revolves around Christian mythology and issues of social responsibility. These structures can also be viewed from the point of view of folk psychology and naïve sociological assumptions.

Standardization efforts: The relationship between knowledge dimensions, search processes and innovation outcomes

We explore how a standardization effort (i.e., when a firm pursues standards to further innovation) involves different search processes for knowledge and innovation outcomes. Using an inductive case study of Vanke, a leading Chinese property developer, we show how varying degrees of knowledge complexity and codification combine to produce a typology of four types of search process: active, integrative, decentralized and passive, resulting in four types of innovation outcome: modular, radical, incremental and architectural. We argue that when the standardization effort in a firm involves highly codified knowledge, incremental and architectural innovation outcomes are fostered, while modular and radical innovations are hindered. We discuss how standardization efforts can result in a second-order innovation capability, and conclude by calling for comparative research in other settings to understand how standardization efforts can be suited to different types of search process in different industry contexts.

Estrogen-induced synthesis of riboflavin-binding protein in immature chicks kinetics and hormonal specificity

The kinetics of estrogen-induced elevation in the plasma concentration of riboflavin-binding protein, a minor yolk constituent, was investigated in immature male chicks, using a specific and sensitive radioimmunoassay proceudre. Following a single injection of the hormone, the plasma riboflavin-binding protein content was enhanced several-fold at 6 h. reaching peak levels around 48 h and declining thereafter. A two-fold amplication of the response was evident on secondary stimulation with the hormone. A 4-h lag phase prior to onset of induction was noticed during both primary and secondary stimulat ions with the steroid hormone. The magnitude of the response was dependent on the hormonal dose whereas the initial lag phase and the time of peak riboflavin-binding protein accumulation were unaltered within the range of hormonal doses tested. The half-life of riboflavin-binding protein in the circulation was 10 h, as calculated from measurement of the rate of disappearance of exogenously administered 125I-labelled protein. Simultaneous administration of progestrone did bot affect the kinetics of riboflavin-binding protein production. On the other hand, the antiestrogens, cis- and trans-clomiphene citrates, given 30 min prior to estrogen and cycloheximide, effectively countered the hormone-induced riboflavin-binding protein elaboration. Both progesterone and the anti-esterogens per se were completely ineffective in substituting for estrogen in the inductive ptrocess.

A heterogeneous computing approach to simulation of the Heston Stochastic Volatility Model

Stochastic volatility models are of fundamental importance to the pricing of derivatives. One of the most commonly used models of stochastic volatility is the Heston Model in which the price and volatility of an asset evolve as a pair of coupled stochastic differential equations. The computation of asset prices and volatilities involves the simulation of many sample trajectories with conditioning. The problem is treated using the method of particle filtering. While the simulation of a shower of particles is computationally expensive, each particle behaves independently making such simulations ideal for massively parallel heterogeneous computing platforms. In this paper, we present our portable Opencl implementation of the Heston model and discuss its performance and efficiency characteristics on a range of architectures including Intel cpus, Nvidia gpus, and Intel Many-Integrated-Core (mic) accelerators.

Human error risk management methodology for rail crack incidents

The paper presents an innovative approach to modelling the causal relationships of human errors in rail crack incidents (RCI) from a managerial perspective. A Bayesian belief network is developed to model RCI by considering the human errors of designers, manufactures, operators and maintainers (DMOM) and the causal relationships involved. A set of dependent variables whose combinations express the relevant functions performed by each DMOM participant is used to model the causal relationships. A total of 14 RCI on Hong Kong’s mass transit railway (MTR) from 2008 to 2011 are used to illustrate the application of the model. Bayesian inference is used to conduct an importance analysis to assess the impact of the participants’ errors. Sensitivity analysis is then employed to gauge the effect the increased probability of occurrence of human errors on RCI. Finally, strategies for human error identification and mitigation of RCI are proposed. The identification of ability of maintainer in the case study as the most important factor influencing the probability of RCI implies the priority need to strengthen the maintenance management of the MTR system and that improving the inspection ability of the maintainer is likely to be an effective strategy for RCI risk mitigation.

Characteristics of nonideal operational amplifiers

The departures of the operational amplifiers (OA's) from the ideal performance and their effect on VCV's in the inverting and noninverting mode are discussed. It is found that for the same ideal gain, the bandwidths for the inverting and noninverting modes are different, the former being less. Complete equivalent circuits describing the frequency dependance of the input and output impedances for both modes are given. In particular, the output impedance is shown to be inductive for the frequencies of interest, and this is also confirmed by experimental results.