Sökning: "Bayesiansk regression"

Visar resultat 1 - 5 av 15 uppsatser innehållade orden Bayesiansk regression.

  1. 1. Predicting Patent Data using Wavelet Regression and Bayesian Machine Learning

    Master-uppsats, KTH/Matematik (Avd.)

    Författare :Mattias Martinsen; [2023]
    Nyckelord :Wavelet; Regression; Bayesian network; Prediction; Patent; Machine Learning; Wavelet; Regression; Bayesiskt nätverk; Predicering; Patent; Maskininlärning;

    Sammanfattning : Patents are a fundamental part of scientific and engineering work, ensuringprotection of inventions owned by individuals or organizations. Patents areusually made public 18 months after being filed to a patent office, whichmeans that current publicly available patent data only provides informationabout the past. LÄS MER

  3. 2. A Predictive Analysis of Customer Churn

    Kandidat-uppsats, KTH/Matematisk statistik

    Författare :Olivia Eskils; Anna Backman; [2023]
    Nyckelord :Churn prediction; CRM; optimization; applied mathematics; machine learning; gradient boosting; random forest; logistic regression; insurance industry; Kundbortfall; CRM; optimering; tillämpad matematik; maskininlärning; gradient boosting; random forest; logistisk regression; försäkringsbranschen;

    Sammanfattning : Churn refers to the discontinuation of a contract; consequently, customer churn occurs when existing customers stop being customers. Predicting customer churn is a challenging task in customer retention, but with the advancements made in the field of artificial intelligence and machine learning, the feasibility to predict customer churn has increased. LÄS MER

  4. 3. Auto-Tuning Apache Spark Parameters for Processing Large Datasets

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Shidi Zhou; [2023]
    Nyckelord :Apache Spark; Cloud Environment; Spark Configuration Parameter; Resource Utilization; Ridge Regression; Elastic Net; Random Forest; Deep Neural Network; Bayesian Optimization; Particle Swarm Optimization.; Apache Spark; Molnmiljö; Apache Spark konfigurationsparameter; Resursutnyttjande; Ridge-regression; Elastisk nät; Slumpskog; Djupt neuralt nätverk; Bayesiansk optimering; Partikelsvärmsoptimering.;

    Sammanfattning : Apache Spark is a popular open-source distributed processing framework that enables efficient processing of large amounts of data. Apache Spark has a large number of configuration parameters that are strongly related to performance. Selecting an optimal configuration for Apache Spark application deployed in a cloud environment is a complex task. LÄS MER

  5. 4. Favourable Opportunities in Sports Betting - A Statistical Approach to Football Goals in the Premier League

    Kandidat-uppsats, KTH/Matematisk statistik

    Författare :Fredrik Lindau; Gustaf Carle; [2022]
    Nyckelord :statistics; Poisson distribution; Negative Binomial distribution; Bayesian regression; football; Premier League; sports betting; market efficiency; probability theory; estimations; statistik; Poisson fördelning; Negativ Binomial fördelning; Bayesiansk regression; fotboll; Premier League; betting; marknadseffektivitet; sannolikhetsteori; skattningar;

    Sammanfattning : The premise of this report is to delve into sports betting and whether favourable opportunities can be found, more specifically focusing on over and under odds for number of goals scored in football games of the Premier League. Using historical data from football matches several models are developed, the characteristics of goals warranting the use of probability based Poisson and Negative Binomial models, as well as Bayesian Poisson regression for goal predictions. LÄS MER

  7. 5. Gaussian Process Methods for Estimating Radio Channel Characteristics

    Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Anton Ottosson; Viktor Karlstrand; [2020]
    Nyckelord :Gaussian processes; deep Gaussian processes; Bayesian methods; transfer functions; channel estimation; coher-ence bandwidth; kernel;

    Sammanfattning : Gaussian processes (GPs) as a Bayesian regressionmethod have been around for some time. Since proven advant-ageous for sparse and noisy data, we explore the potential ofGaussian process regression (GPR) as a tool for estimating radiochannel characteristics. LÄS MER