No description available.

This study examines how students without formal statistics draw conclusions based on statistical data. The study refers to how pupil draw conclusions based on existing data, reason within the subject statistics and which aspects pupil may have difficulty in perceiving. To investigate this, the following research question were used: How do Swedish students without formal statistical training express informal statistical inferences?     This study is based on informal statistical inference which means that how to draw conclusions based on already existing data about what will happen in the next step. To collect data for this study, the data collection method focusgroup interviews with students in year 5 was used. The collected data were compiled, analyzed and compared with previous research. The results showed that students have difficulty drawing conclusions based on the existing data because they do not see it in a larger context.

No description available.

The ability to generate inferences is a skill that is necessary to fully comprehend a text and understand the intentions, behaviors, and emotions of a conversational partner. Individuals with Asperger syndrome (AS) have been shown to demonstrate significant difficulty in inference generation in both social contexts and in reading comprehension. Although, the reciprocity of the four components of literacy (reading, writing, listening, and speaking) has been established in the literature (Bradley & Bryant, 1983; Catts & Kamhi, 2005; Englert & Thomas, 1987; Gillon & Dodd, 1995; Hiebert, 1980; Kroll, 1981; Ruddell & Ruddell, 1994); the relationship between inference generation in reading and social inference generation is not well understood. The present study investigated the efficacy of a language-focused reading inference strategy intervention (ACT & Check Strategy) on the general reading comprehension, inference generation in reading, social inference, and metacognitive ability of adults with AS. Twenty-five adults with AS were randomly assigned to either a treatment or a control group. The treatment group participants were divided into groups of 3-4 based on their availability and preferred location for treatment resulting in a total of 4 groups. Each group met in one-hour sessions twice a week for a total of six weeks. When controlling for pretest scores, the treatment group was found to perform significantly better on one measure of inference generation in reading and metacognitive ability compared to the control group. Significant differences between groups were not found in two measures of inference generation in reading comprehension or social inference ability.

The remarkable ability of the human brain to draw an accurate percept from imprecise sensory information is not well understood. Bayesian inference provides an optimal means for drawing perceptual conclusions from sensorineural activity. This approach has frequently been applied to visual and auditory studies but only rarely to studies of tactile perception. We explored whether a Bayesian observer model could replicate fundamental aspects of human tactile spatial perception. The model consisted of an encoder that simulated sensorineural responses with Poisson statistics followed by a decoder that interpreted the observed firing rates. We compared the performance of our Bayesian observer on a battery of tactile tasks to human participant data collected previously by our laboratory and others. The Bayesian observer replicated human performance trends on three spatial acuity tasks: classic two-point discrimination (C2PD), sequential two-point discrimination (S2PD), and two-point orientation discrimination (2POD). We confirmed the widely reported observation that C2PD is the least reliable method of assessing tactile acuity due presumably to the presence of non-spatial cues. Additionally, the Bayesian observer performed similarly to humans on raised letter and Braille character-recognition tasks. The Bayesian observer further replicated two illusions previously reported in humans: an adaptation-induced repulsion illusion and an orientation anisotropy illusion. Taken together, these results suggest that human tactile spatial perception may arise from a Bayesian-like decoder that is unaware of the precise characteristics of its inputs. / Thesis / Master of Science (MSc)

The Poisson distribution is commonly used in analyzing count data, and many insurance companies are interested in studying the related risk models and ruin probability theory. Over the past century, many different bivariate models have been developed in the literature. The bivariate Poisson distribution was first introduced by Campbell (1934) for modelling bivariate accident data. However, in some situations, a given dataset may possess over-dispersion compared to Poisson distribution which moti- vated researchers to develop alternative models to handle such situations. In this regard, Minkova and Balakrishnan (2014a) developed the Type I bivariate Polya- Aeppli distribution by using compounding with Geometric random variables and the trivariate reduction method. Inference for this Type I bivariate Polya-Aeppli distribution is the topic of this thesis. The parameters in a model are used to describe and summarize a given sample within a specific distribution. So, their estimation becomes important and the goal of estimation theory is to seek a method to find estimators for the parameters of interest that have some good properties. There exist many methods of finding estimators such as Method of Moments, Bayesian estimators, Least Squares, and Maximum Likelihood Estimators (MLEs). Each method of estimation has its own strength and weakness (Casella and Berger (2008)). Minkova and Balakrishnan (2014a) discussed the moment estimation of the parameters of the Type I bivariate Polya-Aeppli dis- tribution. In this thesis, we develop the likelihood inference for this model. A simulation study is carried out with various parameter settings. The obtained results show that the MLEs require more computational time compared to Moment estimation. However, Method of Moments (MoM) did not result in good estimates for all the simulation settings. In terms of mean squared error and bias, we observed that MLEs performed, in most of the settings, better than MoM. Finally, we apply the Type I bivariate Polya-Aeppli model to a real dataset containing the frequencies of railway accidents in two subsequent six year periods. We also carry out some hypothesis tests using the Wald test statistic. From these results, we conclude that the two variables belong to the same univariate Polya-Aeppli distribution but are correlated. / Thesis / Master of Science (MSc)

In the era of artificial intelligence and machine learning, AI/ML inference tasks have become exceedingly popular. However, executing these workloads on dedicated hardware may not be feasible for many users due to high maintenance costs, varying load patterns, and time to production. Furthermore, ML inference workloads are stateless, and most of them are not extremely latency sensitive. For example, tasks such as fake review removal, abusive language detection, tweet classification, image tagging, and free-tier-chat-bots do not require real-time inference. All these characteristics make serverless platforms a good fit for deployment, and in this work, we identify the bottlenecks involved in hosting these inference jobs on serverless and optimize serverless for better performance and resource utilization. Specifically, we identify model loading and model memory duplication as major bottlenecks in Serverless Inference, and to address these problems, we propose a new approach that rethinks the way we serve FaaS requests. To support this design, we employ a hybrid scaling approach to implement the autoscale feature of serverless. / Master of Science / Most modern software applications leverage the power of machine learning to incorporate intelligent features. For instance, platforms like Yelp employ machine learning algorithms to detect fake reviews, while intelligent chatbots such as ChatGPT provide interactive conversations. Even Netflix relies on machine learning to recommend personalized content to its users. The process of creating these machine learning services involves several stages, including data collection, model training using the collected data, and serving the trained model to deploy the service. This final stage, known as inference, is crucial for delivering real-time predictions or responses to user queries. In our research, we focus on selecting serverless computing as the preferred infrastructure for deploying these popular inference workloads. Serverless, also referred to as Function as a Service (FaaS), is an execution paradigm in cloud computing that allows users to efficiently run their code by providing scalability, elasticity and fine-grained billing. In this work we identified, model loading and model memory duplication as major bottlenecks in Serverless Inference. To solve these problems we propose a new approach which rethinks the way we serve FaaS requests. To support this design we use a hybrid scaling approach to implement the autoscale feature of serverless.

Causal inference methods have been applied in various fields where researchers want to establish causal effects between different phenomena. The goal of causal inference is to estimate treatment effects by comparing outcomes had units received treatment versus outcomes had units not received treatment. We focus on estimating treatment effects in three different projects. We first proposed linear unbiased estimators (LUEs) for general causal effects under the assumption that treatment effects are additive. Under the assumption of additivity, the set of estimands considered grows as contrasts in exposures are now equivalent. Furthermore, we identified a subset of LUEs that forms an affine basis for LUEs, and we characterized LUEs with minimum integrated variance through defining conditions on the support of the estimator. We also estimated the effect of drug-induced homicide (DIH) prosecutions reported by the media on unintentional drug overdose deaths, which have never been empirically assessed, using various models. Using a difference-in-differences-like logistic generalized additive model (GAM) with smoothed time effects where we assumed a constant treatment effect, we found that DIH prosecutions reported by the media were associated with a potential harmful effect (risk ratio: 1.064; 95% CI: (1.012, 1.118)) on drug overdose deaths. Upon further research, however, there are potential issues using a constant treatment effect model in a setting where treatment is staggered and treatment effects are heterogeneous. Therefore, we also used a GAM with a linear link function where we assumed that treatment effects may depend on the treatment duration. With this second model, we estimated a risk ratio for having any DIH prosecutions reported by the media of 0.956 (95% CI: (0.824, 1.110)) and a risk ratio of 0.986 (95% CI: (0.973, 0.999)) for the effect of being exposed to DIH prosecutions reported by the media for each additional six months. Despite being statistically significant, the effects were not practically significant. However, the results call for further research on the effect of DIH prosecutions on drug overdose deaths. Lastly, we shift our focus to Structural Nested Mean Models (SNMMs). We extended SNMMs to a new class of estimators which estimate treatment effects of different treatment regimes in the risk ratio scale---the Structural Nested Risk Ratio Model (SNRRM). We further generalized previous work on SNMMs by estimating treatment effects by modeling a function of treatment, which we choose to be any function that can be modeled by generalized linear models, as opposed to just a model for treatment initiation. We applied SNRRMs to estimate the effect of DIH prosecutions reported by the media on drug overdose deaths.

Recently, formal methods have gained significant traction for describing, checking, and synthesizing the behaviors of cyber-physical systems. Among these methods, temporal logics stand out as they offer concise mathematical formulas to express desired system properties. In this thesis, our focus revolves around two primary applications of temporal logics in describing the behavior of autonomous system. The first involves integrating temporal logics with machine learning techniques to deduce a temporal logic specification based on the system's execution traces. The second application concerns using temporal logics to define traffic rules and develop a control scheme that guarantees compliance with these rules for autonomous vehicles. Ultimately, our objective is to combine these approaches, infer a specification that characterizes the desired behaviors of autonomous vehicles, and ensure that these behaviors are upheld during runtime. In the first study of this thesis, our focus is on learning Signal Temporal Logic (STL) specifications from system execution traces. Our approach involves two main phases. Initially, we address an offline supervised learning problem, leveraging the availability of system traces and their corresponding labels. Subsequently, we introduce a time-incremental learning framework. This framework is designed for a dataset containing labeled signal traces with a common time horizon. It provides a method to predict the label of a signal as it is received incrementally over time. To tackle both problems, we propose two decision tree-based approaches, with the aim of enhancing the interpretability and classification performance of existing methods. The simulation results demonstrate the efficiency of our proposed approaches. In the next study, we address the challenge of guaranteeing compliance with traffic rules expressed as STL specifications within the domain of autonomous driving. Our focus is on developing control frameworks for a fully autonomous vehicle operating in a deterministic or stochastic environment. Our frameworks effectively translate the traffic rules into high-level decisions and accomplish low-level vehicle control with good real-time performance. Compared to existing literature, our approaches demonstrate significant enhancements in terms of runtime performance. / 2025-01-17T00:00:00Z

No description available.