Essays about: "Distribution models"

Showing result 1 - 5 of 1024 essays containing the words Distribution models.

  1. 1. Variational AutoEncoders and Differential Privacy : balancing data synthesis and privacy constraints

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Baptiste Bremond; [2024]
    Keywords : TVAE; Differential privacy; Tabular data; Synthetic data; DP-SGD; TVAE; differentiell integritet; tabelldata; syntetiska data; DP-SGD;

    Abstract : This thesis investigates the effectiveness of Tabular Variational Auto Encoders (TVAEs) in generating high-quality synthetic tabular data and assesses their compliance with differential privacy principles. The study shows that while TVAEs are better than VAEs at generating synthetic data that faithfully reproduces the distribution of real data as measured by the Synthetic Data Vault (SDV) metrics, the latter does not guarantee that the synthetic data is up to the task in practical industrial applications. READ MORE

  2. 2. Multi-Parameter Modelling of Surface Electromyography Data

    University essay from Lunds universitet/Avdelningen för Biomedicinsk teknik

    Author : Ahmad Alosta; Josef Djärf; [2024]
    Keywords : Surface electromyography; modelling; Python; sEMG; decomposition; neuroengineering; motor unit; muscle; simulation; Technology and Engineering;

    Abstract : Ytelektromyografi (sEMG) mäter skelettmuskelfunktionen genom att registrera muskelaktivitet från hudens yta. Tekniken kan användas för att diagnostisera neuromuskulära sjukdomar och som ett hjälpmedel vid rehabilitering, biomedicinsk forskning och för interaktion mellan människa och dator. READ MORE

  3. 3. Measuring the Utility of Synthetic Data : An Empirical Evaluation of Population Fidelity Measures as Indicators of Synthetic Data Utility in Classification Tasks

    University essay from Karlstads universitet/Institutionen för matematik och datavetenskap (from 2013)

    Author : Alexander Florean; [2024]
    Keywords : Synthetic Data; Machine Learning; Population Fidelity Measures; Utility Metrics; Synthetic Data Quality Evaluation; Classification Algorithms; Utility Estimation; Data Privacy; Artificial Intelligence; Experiment Framework; Model Performance Assessment; Syntetisk Data; Maskininlärning; Population Fidelity Mätvärden; Användbarhetsmätvärden; Kvalitetsutvärdering av Syntetisk Data; Klassificeringsalgoritmer; Användbarhetsutvärdering; Dataintegritet; Artificiell Intelligens; AI; Experiment Ramverk; Utvärdering av Modellprestanda;

    Abstract : In the era of data-driven decision-making and innovation, synthetic data serves as a promising tool that bridges the need for vast datasets in machine learning (ML) and the imperative necessity of data privacy. By simulating real-world data while preserving privacy, synthetic data generators have become more prevalent instruments in AI and ML development. READ MORE

  4. 4. Are Distributional Variables Useful for Forecasting With the Phillips Curve?

    University essay from Handelshögskolan i Stockholm/Institutionen för nationalekonomi

    Author : Elsa Rosengren; Pippa Johns; [2024]
    Keywords : Distributional Variables; Heterogeneous Agents; Inflation; Phillips Curve; Inequality;

    Abstract : Does information on the distribution of wealth and income help us forecast aggregate macroeconomic variables? In this thesis, we study how adding such distributional variables to a standard forecasting model affects the forecast accuracy, in the context of inflation forecasting. Using the simulated inflation forecasting approach of Atkeson and Ohanian (2001), we perform a horse race between a textbook NAIRU Phillips curve to an extension augmented with variables from the wealth and income distributions. READ MORE

  5. 5. The Role of Uni- and Multivariate Bias Adjustment Methods for Future Hydrological Projections and Subsequent Decision-Making

    University essay from Uppsala universitet/Luft-, vatten- och landskapslära

    Author : Anna Merle Liebenehm-Axmann; [2024]
    Keywords : Bias adjustment methods; future hydrological climate projections; statistical analysis; future streamflow analysis; biasjusteringsmetoder; framtida hydrologiska projektioner; statistisk analys; framtida vattenförings analys;

    Abstract : Climate models are essential for generating future climate projections. However, due to simplifications, the models can produce systematic differences between output and reality, which is referred to as model bias. Bias adjustment methods aim to reduce this error, which is important for making future projections more reliable. READ MORE