Bayesian Gmm

I study a semiparametric Bayesian method for over-identified moment condition models. A mixture of parametric distributions with random weights is used to flexibly model an unknown data generating process. The random mixture weights are defined by the exponential tilting projection method to ensure that the joint distribution of the data distribution and the structural parameters are internally consistent with the moment restrictions. In this framework, I make several contributions to Bayesian estimation and inference, as well as model specification. First, I develop simulation-based posterior sampling algorithms based on Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods. Second, I provide a method to compute the marginal likelihood and use it for Bayesian model selection (moment selection) and model averaging. Lastly, I extend the scope of Bayesian analysis for moment condition models. These generalizations include dynamic moment condition models with time-dependent data and moment condition models with exogenous dynamic latent variables

The Evolution of First Person Vision Methods: A Survey

The emergence of new wearable technologies such as action cameras and smart-glasses has increased the interest of computer vision scientists in the First Person perspective. Nowadays, this field is attracting attention and investments of companies aiming to develop commercial devices with First Person Vision recording capabilities. Due to this interest, an increasing demand of methods to process these videos, possibly in real-time, is expected. Current approaches present a particular combinations of different image features and quantitative methods to accomplish specific objectives like object detection, activity recognition, user machine interaction and so on. This paper summarizes the evolution of the state of the art in First Person Vision video analysis between 1997 and 2014, highlighting, among others, most commonly used features, methods, challenges and opportunities within the field.Comment: First Person Vision, Egocentric Vision, Wearable Devices, Smart Glasses, Computer Vision, Video Analytics, Human-machine Interactio

High accuracy context recovery using clustering mechanisms

This paper examines the recovery of user context in indoor environmnents with existing wireless infrastructures to enable assistive systems. We present a novel approach to the extraction of user context, casting the problem of context recovery as an unsupervised, clustering problem. A well known density-based clustering technique, DBSCAN, is adapted to recover user context that includes user motion state, and significant places the user visits from WiFi observations consisting of access point id and signal strength. Furthermore, user rhythms or sequences of places the user visits periodically are derived from the above low level contexts by employing state-of-the-art probabilistic clustering technique, the Latent Dirichiet Allocation (LDA), to enable a variety of application services. Experimental results with real data are presented to validate the proposed unsupervised learning approach and demonstrate its applicability.<br /

Testing the nature of dark compact objects: a status report

Very compact objects probe extreme gravitational fields and may be the key to understand outstanding puzzles in fundamental physics. These include the nature of dark matter, the fate of spacetime singularities, or the loss of unitarity in Hawking evaporation. The standard astrophysical description of collapsing objects tells us that massive, dark and compact objects are black holes. Any observation suggesting otherwise would be an indication of beyond-the-standard-model physics. Null results strengthen and quantify the Kerr black hole paradigm. The advent of gravitational-wave astronomy and precise measurements with very long baseline interferometry allow one to finally probe into such foundational issues. We overview the physics of exotic dark compact objects and their observational status, including the observational evidence for black holes with current and future experiments.Comment: 76 pages + references. Invited review article for Living Reviews in Relativity. v3: Overall improvements and references added, a few typos corrected. Version to appear in LR

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Modeling Semi-Bounded Support Data using Non-Gaussian Hidden Markov Models with Applications

With the exponential growth of data in all formats, and data categorization rapidly becoming one of the most essential components of data analysis, it is crucial to research and identify hidden patterns in order to extract valuable information that promotes accurate and solid decision making. Because data modeling is the first stage in accomplishing any of these tasks, its accuracy and consistency are critical for later development of a complete data processing framework. Furthermore, an appropriate distribution selection that corresponds to the nature of the data is a particularly interesting subject of research. Hidden Markov Models (HMMs) are some of the most impressively powerful probabilistic models, which have recently made a big resurgence in the machine learning industry, despite having been recognized for decades. Their ever-increasing application in a variety of critical practical settings to model varied and heterogeneous data (image, video, audio, time series, etc.) is the subject of countless extensions. Equally prevalent, finite mixture models are a potent tool for modeling heterogeneous data of various natures. The over-use of Gaussian mixture models for data modeling in the literature is one of the main driving forces for this thesis. This work focuses on modeling positive vectors, which naturally occur in a variety of real-life applications, by proposing novel HMMs extensions using the Inverted Dirichlet, the Generalized Inverted Dirichlet and the BetaLiouville mixture models as emission probabilities. These extensions are motivated by the proven capacity of these mixtures to deal with positive vectors and overcome mixture models’ impotence to account for any ordering or temporal limitations relative to the information. We utilize the aforementioned distributions to derive several theoretical approaches for learning and deploying Hidden Markov Modelsinreal-world settings. Further, we study online learning of parameters and explore the integration of a feature selection methodology. Extensive experimentation on highly challenging applications ranging from image categorization, video categorization, indoor occupancy estimation and Natural Language Processing, reveals scenarios in which such models are appropriate to apply, and proves their effectiveness compared to the extensively used Gaussian-based models