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A statistical model of neural network learning via the Cramer–Rao lower bound
The neural networks (NN) remain as black boxes, albeit their quite successful stories everywhere. It is mainly because they provide only the complex...
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GPS data on tourists: a spatial analysis on road networks
This paper proposes a spatial point process model on a linear network to analyse cruise passengers’ stop activities. It identifies and models...
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Second-order and local characteristics of network intensity functions
The last decade has witnessed an increase of interest in the spatial analysis of structured point patterns over networks whose analysis is...
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Neural Network for the Statistical Process Control of HVAC Systems in Passenger Rail Vehicles
In the rail industry, coach temperature regulation has become a crucial task to improve passenger thermal comfort. Over the past few years, European...
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Regularised Semi-parametric Composite Likelihood Intensity Modelling of a Swedish Spatial Ambulance Call Point Pattern
Motivated by the development of optimal dispatching strategies for prehospital resources, we model the spatial distribution of ambulance call events...
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Characterization of topic-based online communities by combining network data and user generated content
This study proposes a model for characterizing online communities by combining two types of data: network data and user-generated-content (UGC). The...
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Classification of periodic arrivals in event time data for filtering computer network traffic
Periodic patterns can often be observed in real-world event time data, possibly mixed with non-periodic arrival times. For modelling purposes, it is...
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Network Topology Inference
Network graphs are constructed in all sorts of ways and to varying levels of completeness. In some settings, there is little if any uncertainty in...
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Using Projection-Based Clustering to Find Distance- and Density-Based Clusters in High-Dimensional Data
For high-dimensional datasets in which clusters are formed by both distance and density structures (DDS), many clustering algorithms fail to identify...
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Models to Support Forest Inventory and Small Area Estimation Using Sparsely Sampled LiDAR: A Case Study Involving G-LiHT LiDAR in Tanana, Alaska
A two-stage hierarchical Bayesian model is developed and implemented to estimate forest biomass density and total given sparsely sampled LiDAR and...
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Hybrid semiparametric Bayesian networks
This paper presents a new class of Bayesian networks called hybrid semiparametric Bayesian networks, which can model hybrid data (discrete and...
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Estimation of relative risk for events on a linear network
Motivated by the study of traffic accidents on a road network, we discuss the estimation of the relative risk, the ratio of rates of occurrence of...
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Mathematical Models for Network Graphs
So far in this book, the emphasis has been almost entirely focused upon methods, to the exclusion of modeling—methods for constructing network...
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Density and distribution evaluation for convolution of independent gamma variables
Convolutions of independent gamma variables are encountered in many applications such as insurance, reliability, and network engineering. Accurate...
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Forecasting Natural Gas Prices with Spatio-Temporal Copula-Based Time Series Models
In this work, we model and forecast commodity price time series using multivariate copula-based time series models. In particular, we consider the...
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Joint Microbial and Metabolomic Network Estimation with the Censored Gaussian Graphical Model
Joint analysis of microbiome and metabolomic data represents an imperative objective as the field moves beyond basic microbiome association studies...
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Statistical Models for Network Graphs
The network models discussed in the previous chapter serve a variety of useful purposes. Yet for the purpose of statistical model building, they come...
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Descriptive Analysis of Network Graph Characteristics
In the study of a given complex system, questions of interest can often be re-phrased in a useful manner as questions regarding some aspect of the...
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Poisson Edge Growth and Preferential Attachment Networks
When modeling a directed social network, one choice is to use the traditional preferential attachment model, which generates power-law tail...
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The Relative Fit measure for evaluating a blockmodel
A blockmodel is a network in which the nodes are clusters of equivalent (in terms of the structure of the links connecting) nodes in the network...
