Essays about: "Bayesian Statistics"
Showing result 1 - 5 of 67 essays containing the words Bayesian Statistics.
-
1. Uncertainty Quantification in Deep Learning for Breast Cancer Classification in Point-of-Care Ultrasound Imaging
University essay from Lunds universitet/Matematik LTHAbstract : Breast cancer is the most common type of cancer worldwide with an estimate of 2.3 million new cases in 2020, and the number one cause of cancer-related deaths in women. READ MORE
-
2. Regularization Methods and High Dimensional Data: A Comparative Study Based on Frequentist and Bayesian Methods
University essay from Lunds universitet/Statistiska institutionenAbstract : As the amount of high dimensional data becomes increasingly accessible and common, the need for reliable methods to combat problems such as overfitting and multicollinearity increases. Models need to be able to manage large data sets where predictor variables often outnumber the amount of observations. READ MORE
-
3. Parameter Inference for Stochastic Models of Gene Expression in Eukaryotic Cells
University essay from Uppsala universitet/Institutionen för informationsteknologiAbstract : Simulation models are often used to study a system or phenomenon. However, before a simulation model can be used, its parameter needs to be fit to mimic observed data. This is called the parameter inference problem. READ MORE
-
4. Approximate Bayesian Computation for Data-Driven Epidemiological Models
University essay from Uppsala universitet/Institutionen för informationsteknologiAbstract : Epidemiological models can help us to understand the spread of pathogens in a population. Fitting these mathematical models to epidemiological data can be a difficult task due to uncertain or missing data. READ MORE
-
5. LDPC DropConnect
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Machine learning is a popular topic that has become a scientific research tool in many fields. Overfitting is a common challenge in machine learning, where the model fits the training data too well and performs poorly on new data. READ MORE