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Inference on high-dimensional implicit dynamic models using a guided intermediate resampling filter
We propose a method for inference on moderately high-dimensional, nonlinear, non-Gaussian, partially observed Markov process models for which the...
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Analysis of stochastic gradient descent in continuous time
Stochastic gradient descent is an optimisation method that combines classical gradient descent with random subsampling within the target functional....
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Bayesian Hidden Markov Models for Early Warning
We show how Bayesian hidden Markov models may be employed to build early warning systems of particular risky events. The adopted model formulation...
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Dynamics of Sedentary Behaviours and System-Based Approach: Future Challenges and Opportunities in the Life-Course Epidemiology of Sedentary Behaviours
This chapter challenges our current thinking about sedentary behaviour and offers new paradigms to move forward to understand the complex nature of...
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Model Specification and Model Performance
The topics of model, parameter specification and design are discussed in this chapter. We start by discussing model components, such us design...
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DBGAN: A Data Balancing Generative Adversarial Network for Mobility Pattern Recognition
Mobility pattern recognition is a central aspect of transportation and data mining research. Despite the development of various machine learning...
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A Dynamic Model for Ordinal Time Series: An Application to Consumers’ Perceptions of Inflation
This article discusses an innovative model for time series ordinal data, which develops the well-established CUB model to allow for time-varying...
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Urban Land Use Analysis
This chapter introduces concepts and methods for land use analyses, including land use classification systems, land use inventory and compatibility...
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Analysis of the Battery Level in Complex Wireless Sensor Networks Using a Two Time Scales Second Order Fluid Model
Battery operated Wireless Sensor Networks (WSNs) are currently one of the most important research areas related to applications: the possibility of...
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FLOWER: Viewing Data Flow in ER Diagrams
In data science, data pre-processing and data exploration require various convoluted steps such as creating variables, merging data sets, filtering...
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Local Heterogeneities in Population Growth and Decline. A Spatial Analysis of Italian Municipalities
Spatially unequal demographic dynamics lead to a progressive fragility of a territory and its socio-economic system. In Italy, municipalities...
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Clustering by deep latent position model with graph convolutional network
With the significant increase of interactions between individuals through numeric means, clustering of nodes in graphs has become a fundamental...
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Structure and Dynamics of Analytic Philosophy
In this chapter, we investigate by different types of citation analysis the structure and dynamics of Late Analytic Philosophy in order to shed light...
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Computing T-optimal designs via nested semi-infinite programming and twofold adaptive discretization
Modelling real processes often results in several suitable models. In order to be able to distinguish, or discriminate, which model best represents a...
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Minimax weight learning for absorbing MDPs
Reinforcement learning policy evaluation problems are often modeled as finite or discounted/averaged infinite-horizon Markov Decision Processes...
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Bayesian forecasting of multivariate time series: scalability, structure uncertainty and decisions
I discuss recent research advances in Bayesian state-space modeling of multivariate time series. A main focus is on the “decouple/recouple” concept...
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Bespoke Learning to Generate Originally-Absent Training Data
This chapter motivates the need for attending to the class of problems that pertain to a mismatch between the proposed ambition of supervised...
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Exotica
Chapter 10 discussed how a linear approximation to the perennially nonlinear dynamics of infectious...
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Parameter estimation in nonlinear mixed effect models based on ordinary differential equations: an optimal control approach
We present a method for parameter estimation for nonlinear mixed-effects models based on ordinary differential equations (NLME-ODEs). It aims to...
