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Browsing Master's Degree in Data Science by Issue Date
Now showing items 1-14 of 14
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(2017)Data scientific questions face the fundamental trade-off between complexity, generalizability and computational feasibility. The need for quick estimation and evaluation of a vast amount of statistical models has given ...
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(2017)This paper studies a novel particle filter method proposed by Brownlees and Kristensen (2017) for parameter estimation of nonlinear state space models. The particle filter, named Importance Sampling Particle Filter, is tested ...
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(2017)Internet has seen a tremendous growth in the last few years. Because of that, we have a lot of information about most of the things in web. And the usage of Recommendation system has become more important than ever. Rec ...
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(2018)Hierarchical modeling is a practical approach with proven results in modeling real world data. This paper studies Gaussian hierarchical models and methods which exploit the sparse conditional independence structure of such ...
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(2018)The main goal of our Master Project is to predict intraday stock market movements using two different kinds of input features: financial indicators and sentiments from news and tweets. While the former are part of the ...
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(2019)In this thesis we develop a traffic light control agent that can manage traffic lights with the objective to reduce traffic jams, trip time and other traffic metrics in a given network using reinforcement learning. To this ...
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(2020-06-25)In this thesis project I analyse labour flow networks and company control networks in the UK. I observe that these networks exhibit characteristics that are typical of empirical networks, such as heavy-tailed degree ...
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(2020-07-20)In this study, we propose an approach for the extraction of a low-dimensional signal from a collection of text documents ordered over time. The proposed framework foresees the application of Latent Dirichlet Allocation ...
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(2020-08-17)In order to apply statistical learning in the framework of crossed random effects models it is necessary to efficiently compute the Cholesky factor L of the models precision matrix. In this paper we show that for the case ...
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(2021)Normalizing flows are an elegant approximation to generative modelling. It can be shown that learning a probability distribution of a continuous variable X is equivalent to learning a mapping f from the domain where X is ...
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(2021-07-20)Vector autoregression (VAR) models are a popular choice for forecasting of macroeconomic time series data. Due to their simplicity and success at modelling the monetary economic indicators VARs have become a standard tool ...
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(2022-05)Financial institutions are beginning to integrate cryptocurrencies into their payment systems but must ensure to comply with anti-money laundering regulations to avoid facilitating transactions linked to criminal activities. ...
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(2022-05)Bayesian optimization has emerged as an effective and efficient approach for finding the global optimum of highly complex derivative-free black-box functions. It typically models the objective function with Gaussian processes ...
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(2022-06)This thesis presents a general-purpose corpus construction methodology with Twitter data for a given political topic in a given country. It applies the methodology to immigration in Chile from November 2021 to April 2022, ...
Now showing items 1-14 of 14
