Observer performance methods for diagnostic imaging : foundations, modeling, and applications with R-based examples

Diagnostic Biomedical Signal and Image P

📁 Diagnostic Biomedical Signal and Image Processing Applications With Deep Learning Methods: With Deep Learning Methods

✍ Kemal Polat, Saban Öztürk 📂 Library 📅 2023 🏛 Academic Press 🌐 English

Diagnostic Biomedical Signal and Image Processing Applications with Deep Learning Methods presents comprehensive research on both medical imaging and medical signals analysis. The book discusses classification, segmentation, detection, tracking and retrieval applications of non

Model-Based Clustering and Classificatio

📁 Model-Based Clustering and Classification for Data Science: With Applications in R

✍ Charles Bouveyron; Gilles Celeux; T. Brendan Murphy; Adrian E. Raftery 📂 Library 📅 2019 🏛 Cambridge University Press 🌐 English

Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observat

Model-Based Clustering and Classificatio

📁 Model-Based Clustering and Classification for Data Science: With Applications in R

✍ Charles Bouveyron, Gilles Celeux, T. Brendan Murphy, Adrian E. Raftery 📂 Library 📅 2019 🏛 Cambridge University Press 🌐 English

Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observat

Model-Based Clustering and Classificatio

📁 Model-Based Clustering and Classification for Data Science: With Applications in R

✍ Bouveyron C 📂 Library 📅 2019 🏛 Cambridge University Press 🌐 English

Nonlinear Time Series: Theory, Methods a

📁 Nonlinear Time Series: Theory, Methods and Applications with R Examples

✍ Randal Douc, Eric Moulines, David Stoffer 📂 Library 📅 2014 🏛 Chapman and Hall/CRC 🌐 English

This text emphasizes nonlinear models for a course in time series analysis. After introducing stochastic processes, Markov chains, Poisson processes, and ARMA models, the authors cover functional autoregressive, ARCH, threshold AR, and discrete time series models as well as several complementary

Nonlinear Time Series: Theory, Methods a

📁 Nonlinear Time Series: Theory, Methods and Applications with R Examples

✍ Randal Douc, Eric Moulines, David Stoffer 📂 Library 📅 2014 🏛 Chapman and Hall/CRC 🌐 English

Features Describes the major statistical techniques for inferring model parameters, with a focus on the MLE and QMLE Introduces concepts of nonparametric statistics, including smoothing splines Covers HMM models, including Gaussian linear, switching Markovian, and nonlinear state space models Pr