scholarly journals An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Lu Cheng ◽  
Siddharth Ramchandran ◽  
Tommi Vatanen ◽  
Niina Lietzén ◽  
Riitta Lahesmaa ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Regression Model ◽  
Gaussian Process ◽  
Parametric Analysis ◽  
Gaussian Process Regression ◽  
Non Parametric

scholarly journals LonGP: an additive Gaussian process regression model for longitudinal study designs

2018 ◽  
Author(s):  
Lu Cheng ◽  
Siddharth Ramchandran ◽  
Tommi Vatanen ◽  
Niina Lietzen ◽  
Riitta Lahesmaa ◽  
...  
Keyword(s):  
Longitudinal Study ◽  
Longitudinal Data ◽  
Regression Model ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Parametric Modelling ◽  
Time Varying ◽  
Mixed Effect ◽  
Study Designs ◽  
Time Varying Covariates

AbstractMotivationBiomedical research typically involves longitudinal study designs where samples from individuals are measured repeatedly over time and the goal is to identify risk factors (covariates) that are associated with an outcome value. General linear mixed effect models have become the standard workhorse for statistical analysis of data from longitudinal study designs. However, analysis of longitudinal data can be complicated for both practical and theoretical reasons, including difficulties in modelling, correlated outcome values, functional (time-varying) covariates, nonlinear effects, and model inference.ResultsWe present LonGP, an additive Gaussian process regression model for analysis of experimental data from longitudinal study designs. LonGP implements a flexible, non-parametric modelling framework that solves commonly faced challenges in longitudinal data analysis. In addition to inheriting all standard features of Gaussian processes, LonGP can model time-varying random effects and non-stationary signals, incorporate multiple kernel learning, and provide interpretable results for the effects of individual covariates and their interactions. We develop an accurate Bayesian inference and model selection method, and implement an efficient model search algorithm for our additive Gaussian process model. We demonstrate LonGP’s performance and accuracy by analysing various simulated and real longitudinal -omics datasets. Our work is accompanied by a versatile software implementation.AvailabilityLonGP software tool is available athttp://research.cs.aalto.fi/csb/software/longp/[email protected],[email protected]

Anomaly Detection in Video Surveillance via Gaussian Process

2015 ◽  
Vol 29 (06) ◽  
pp. 1555011 ◽  
Author(s):  
Nannan Li ◽  
Xinyu Wu ◽  
Huiwen Guo ◽  
Dan Xu ◽  
Yongsheng Ou ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Regression Model ◽  
Gaussian Process ◽  
Video Surveillance ◽  
Gaussian Process Regression ◽  
Bayesian Regression ◽  
Current Frame ◽  
Motion Patterns ◽  
Online Clustering ◽  
Efficient Calculation

In this paper, we propose a new approach for anomaly detection in video surveillance. This approach is based on a nonparametric Bayesian regression model built upon Gaussian process priors. It establishes a set of basic vectors describing motion patterns from low-level features via online clustering, and then constructs a Gaussian process regression model to approximate the distribution of motion patterns in kernel space. We analyze different anomaly measure criterions derived from Gaussian process regression model and compare their performances. To reduce false detections caused by crowd occlusion, we utilize supplement information from previous frames to assist in anomaly detection for current frame. In addition, we address the problem of hyperparameter tuning and discuss the method of efficient calculation to reduce computation overhead. The approach is verified on published anomaly detection datasets and compared with other existing methods. The experiment results demonstrate that it can detect various anomalies efficiently and accurately.

Application of Gaussian Process Regression Model to Predict Discharge Coefficient of Gated Piano Key Weir

2019 ◽  
Vol 33 (11) ◽  
pp. 3929-3947 ◽  
Author(s):  
Masood Akbari ◽  
Farzin Salmasi ◽  
Hadi Arvanaghi ◽  
Masoud Karbasi ◽  
Davood Farsadizadeh
Keyword(s):  
Regression Model ◽  
Gaussian Process ◽  
Discharge Coefficient ◽  
Gaussian Process Regression ◽  
Piano Key Weir

Validation of Adaptive Gaussian Process Regression Model Used for SIF Prediction

2018 ◽  
Author(s):  
Arvind Keprate ◽  
R. M. Chandima Ratnayake ◽  
Shankar Sankararaman
Keyword(s):  
Regression Model ◽  
Gaussian Process ◽  
Prediction Accuracy ◽  
Experimental Validation ◽  
Gaussian Process Regression ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Data Sets ◽  
Maximum Absolute Error ◽  
Data Points

The main aim of this paper is to perform the validation of the adaptive Gaussian process regression model (AGPRM) developed by the authors for the Stress Intensity Factor (SIF) prediction of a crack propagating in topside piping. For validation purposes, the values of SIF obtained from experiments available in the literature are used. Sixty-six data points (consisting of L, a, c and SIF values obtained by experiments) are used to train the AGPRM, while four independent data sets are used for validation purposes. The experimental validation of the AGPRM also consists of the comparison of the prediction accuracy of AGPRM and Finite Element Method (FEM) relative to the experimentally derived SIF values. Four metrics, namely, Root Mean Square Error (RMSE), Average Absolute Error (AAE), Maximum Absolute Error (MAE), and Coefficient of Determination (R2), are used to compare the accuracy. A case study illustrating the development and experimental validation of the AGPRM is presented. Results indicate that the prediction accuracy of the AGPRM is comparable with and even higher than that of the FEM, provided the training points of the AGPRM are aptly chosen.

scholarly journals Searching for rewards in graph-structured spaces

2019 ◽  
Author(s):  
Eric Schulz ◽  
Charley M Wu
Keyword(s):  
Reinforcement Learning ◽  
Regression Model ◽  
Gaussian Process ◽  
Predictive Model ◽  
Human Behavior ◽  
Model Comparison ◽  
Gaussian Process Regression ◽  
Diffusion Kernel ◽  
Function Learning ◽  
Psychological Models

How do people generalize and explore structured spaces? We study human behavior on a multi-armed bandit task, where rewards are influenced by the connectivity structure of a graph. A detailed predictive model comparison shows that a Gaussian Process regression model using a diffusion kernel is able to best describe participant choices, and also predict judgments about expected reward and confidence. This model unifies psychological models of function learning with the Successor Representation used in reinforcement learning, thereby building a bridge between different models of generalization.

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