Bayesian Nonparametric Approaches for Modelling Stochastic Temporal Events

Modelling stochastic temporal events is a classic machine learning problem that has drawn enormous research attentions over recent decades. Traditional approaches heavily focused on the parametric models that pre-specify model complexity. Comprehensive model comparison and selection are necessary to prevent over-fitting and under-fitting problems. The recently developed Bayesian nonparametric learning framework provides an appealing alternative to traditional approaches. It can automatically learn the model complexity from data. In this thesis, I propose a set of Bayesian nonparametric approaches for stochastic temporal event modelling with the consideration of event similarity, interaction, occurrence time and emitted observation. Specifically, I tackle following three main challenges in the modelling. 1. Data sparsity. Data sparsity problem is common in many real-world temporal event modelling applications, e.g., water pipes failures prediction. A Bayesian nonparametric model that allows pipes with similar behaviour to share failure data is proposed to attain a more effective failure prediction. It is shown that flexible event clustering can help alleviate the data sparsity problem. The clustering process is fully data-driven and it does not require predefining the number of clusters. 2. Event interaction. Stochastic events can interact with each other over time. One event can cause or repel the occurrence of other events. An unexplored theoretical bridge is established between interaction point processes and distance dependent Chinese restaurant process. Hence an integrated model, namely infinite branching model, is developed to estimate point event intensity, interaction mechanism and branching structure simultaneously. 3. Event correlation. The stochastic temporal events are correlated not only between arrival times but also between observations. A novel unified Bayesian nonparametric model that generalizes Hidden Markov model and interaction point processes is constructed to exploit two types of underlying correlation in a well-integrated way rather than individually. The proposed model provides a comprehensive insight into the interaction mechanism and correlation between events. At last, a future vision of Bayesian nonparametric research for stochastic temporal events is highlighted from both application and modelling perspectives

Hidden Markov Models

Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research

A contemporary review on drought modeling using machine learning approaches

Drought is the least understood natural disaster due to the complex relationship of multiple contributory factors. Its beginning and end are hard to gauge, and they can last for months or even for years. India has faced many droughts in the last few decades. Predicting future droughts is vital for framing drought management plans to sustain natural resources. The data-driven modelling for forecasting the metrological time series prediction is becoming more powerful and flexible with computational intelligence techniques. Machine learning (ML) techniques have demonstrated success in the drought prediction process and are becoming popular to predict the weather, especially the minimum temperature using backpropagation algorithms. The favourite ML techniques for weather forecasting include singular vector machines (SVM), support vector regression, random forest, decision tree, logistic regression, Naive Bayes, linear regression, gradient boosting tree, k-nearest neighbours (KNN), the adaptive neuro-fuzzy inference system, the feed-forward neural networks, Markovian chain, Bayesian network, hidden Markov models, and autoregressive moving averages, evolutionary algorithms, deep learning and many more. This paper presents a recent review of the literature using ML in drought prediction, the drought indices, dataset, and performance metrics