Category Archives: Probability And Statistics

Building probabilistic models of physical processes from their deterministic models and some experimental data, with guarantees on the degree of coincidence between the generated model and the real system

September 30, 2015 18:44 ,

Konstantinos Karydis, Ioannis Poulakakis, Jianxin Sun, and Herbert G. Tanner, Probabilistically valid stochastic extensions of deterministic models for systems with uncertainty, The International Journal of Robotics Research September 2015 34: 1278-1295, first published on May 28, 2015. DOI: 10.1177/0278364915576336.

Models capable of capturing and reproducing the variability observed in experimental trials can be valuable for planning and control in the presence of uncertainty. This paper reports on a new data-driven methodology that extends deterministic models to a stochastic regime and offers probabilistic guarantees of model fidelity. From an acceptable deterministic model, a stochastic one is generated, capable of capturing and reproducing uncertain system–environment interactions at given levels of fidelity. The reported approach combines methodological elements from probabilistic model validation and randomized algorithms, to simultaneously quantify the fidelity of a model and tune the distribution of random parameters in the corresponding stochastic extension, in order to reproduce the variability observed experimentally in the physical process of interest. The approach can be applied to an array of physical processes, the models of which may come in different forms, including differential equations; we demonstrate this point by considering examples from the areas of miniature legged robots and aerial vehicles.

Posted in: Probability and statistics, Robot models , Tagged:

Modelling ECGs with sums of gaussians and estimating them through switching Kalman Filters and the likelihood of each mode

September 30, 2015 18:32 ,

Oster, J.; Behar, J.; Sayadi, O.; Nemati, S.; Johnson, A.E.W.; Clifford, G.D., Semisupervised ECG Ventricular Beat Classification With Novelty Detection Based on Switching Kalman Filters, in Biomedical Engineering, IEEE Transactions on , vol.62, no.9, pp.2125-2134, Sept. 2015, DOI: 10.1109/TBME.2015.2402236.

Automatic processing and accurate diagnosis of pathological electrocardiogram (ECG) signals remains a challenge. As long-term ECG recordings continue to increase in prevalence, driven partly by the ease of remote monitoring technology usage, the need to automate ECG analysis continues to grow. In previous studies, a model-based ECG filtering approach to ECG data from healthy subjects has been applied to facilitate accurate online filtering and analysis of physiological signals. We propose an extension of this approach, which models not only normal and ventricular heartbeats, but also morphologies not previously encountered. A switching Kalman filter approach is introduced to enable the automatic selection of the most likely mode (beat type), while simultaneously filtering the signal using appropriate prior knowledge. Novelty detection is also made possible by incorporating a third mode for the detection of unknown (not previously observed) morphologies, and denoted as X-factor. This new approach is compared to state-of-the-art techniques for the ventricular heartbeat classification in the MIT-BIH arrhythmia and Incart databases. F1 scores of 98.3% and 99.5% were found on each database, respectively, which are superior to other published algorithms’ results reported on the same databases. Only 3% of all the beats were discarded as X-factor, and the majority of these beats contained high levels of noise. The proposed technique demonstrates accurate beat classification in the presence of previously unseen (and unlearned) morphologies and noise, and provides an automated method for morphological analysis of arbitrary (unknown) ECG leads.

Posted in: Bayesian filtering , Tagged: , , ,

Good review of similarity measures between elements with semantics

September 30, 2015 16:49 ,

Mohammad Taher Pilehvar, Roberto Navigli, From senses to texts: An all-in-one graph-based approach for measuring semantic similarity, Artificial Intelligence, Volume 228, November 2015, Pages 95-128, ISSN 0004-3702, DOI: 10.1016/j.artint.2015.07.005.

Quantifying semantic similarity between linguistic items lies at the core of many applications in Natural Language Processing and Artificial Intelligence. It has therefore received a considerable amount of research interest, which in its turn has led to a wide range of approaches for measuring semantic similarity. However, these measures are usually limited to handling specific types of linguistic item, e.g., single word senses or entire sentences. Hence, for a downstream application to handle various types of input, multiple measures of semantic similarity are needed, measures that often use different internal representations or have different output scales. In this article we present a unified graph-based approach for measuring semantic similarity which enables effective comparison of linguistic items at multiple levels, from word senses to full texts. Our method first leverages the structural properties of a semantic network in order to model arbitrary linguistic items through a unified probabilistic representation, and then compares the linguistic items in terms of their representations. We report state-of-the-art performance on multiple datasets pertaining to three different levels: senses, words, and texts.

Posted in: Artificial Intelligence, Graph theory, Probability and statistics , Tagged:

Extending probabilistic logic programming with continuous r.v.s, and a nice and brief introduction to programming logic and probabilistic inference

September 30, 2015 16:39 ,

Steffen Michels, Arjen Hommersom, Peter J.F. Lucas, Marina Velikova, A new probabilistic constraint logic programming language based on a generalised distribution semantics, Artificial Intelligence, Volume 228, November 2015, Pages 1-44, ISSN 0004-3702, DOI: 10.1016/j.artint.2015.06.008.

Probabilistic logics combine the expressive power of logic with the ability to reason with uncertainty. Several probabilistic logic languages have been proposed in the past, each of them with their own features. We focus on a class of probabilistic logic based on Sato’s distribution semantics, which extends logic programming with probability distributions on binary random variables and guarantees a unique probability distribution. For many applications binary random variables are, however, not sufficient and one requires random variables with arbitrary ranges, e.g. real numbers. We tackle this problem by developing a generalised distribution semantics for a new probabilistic constraint logic programming language. In order to perform exact inference, imprecise probabilities are taken as a starting point, i.e. we deal with sets of probability distributions rather than a single one. It is shown that given any continuous distribution, conditional probabilities of events can be approximated arbitrarily close to the true probability. Furthermore, for this setting an inference algorithm that is a generalisation of weighted model counting is developed, making use of SMT solvers. We show that inference has similar complexity properties as precise probabilistic inference, unlike most imprecise methods for which inference is more complex. We also experimentally confirm that our algorithm is able to exploit local structure, such as determinism, which further reduces the computational complexity.

Posted in: Artificial Intelligence, Mathematical logic, Probability and statistics , Tagged: , ,

On quickest change detection when the process is modelled with HMMs

September 29, 2015 09:09 ,

Cheng-Der Fuh; Yajun Mei, Quickest Change Detection and Kullback-Leibler Divergence for Two-State Hidden Markov Models, in Signal Processing, IEEE Transactions on , vol.63, no.18, pp.4866-4878, Sept.15, 2015 DOI: 10.1109/TSP.2015.2447506

In this paper, the quickest change detection problem is studied in two-state hidden Markov models (HMM), where the vector parameter θ of the HMM changes from θ0 to θ1 at some unknown time, and one wants to detect the true change as quickly as possible while controlling the false alarm rate. It turns out that the generalized likelihood ratio (GLR) scheme, while theoretically straightforward, is generally computationally infeasible for the HMM. To develop efficient but computationally simple schemes for the HMM, we first discuss a subtlety in the recursive form of the generalized likelihood ratio (GLR) scheme for the HMM. Then we show that the recursive CUSUM scheme proposed in Fuh (Ann. Statist., 2003) can be regarded as a quasi-GLR scheme for pseudo post-change hypotheses with certain dependence structure between pre- and postchange observations. Next, we extend the quasi-GLR idea to propose recursive score schemes in the scenario when the postchange parameter θ1 of the HMM involves a real-valued nuisance parameter. Finally, the Kullback-Leibler (KL) divergence plays an essential role in the quickest change detection problem and many other fields, however it is rather challenging to numerically compute it in HMMs. Here we develop a non-Monte Carlo method that computes the KL divergence of two-state HMMs via the underlying invariant probability measure, which is characterized by the Fredholm integral equation. Numerical study demonstrates an unusual property of the KL divergence for HMM that implies the severe effects of misspecifying the postchange parameter for the HMM.

Posted in: Probability and statistics , Tagged: , , , ,

A novel non-linear bayesian filter for continuous time estimation with a nice comparison to discrete-time filters

July 30, 2015 16:55 ,

Atiyeh Ghoreyshi and Terence D. Sanger, A Nonlinear Stochastic Filter for Continuous-Time State Estimation, IEEE Transactions on Automatic Control, vol. 60, no. 8, DOI: 10.1109/TAC.2015.2409910.

Nonlinear filters produce a nonparametric estimate of the probability density of state at each point in time. Currently known nonlinear filters include Particle Filters and the Kushner equation (and its un-normalized version: the Zakai equation). However, these filters have limited measurement models: Particle Filters require measurement at discrete times, and the Kushner and Zakai equations only apply when the measurement can be represented as a function of the state. We present a new nonlinear filter for continuous-time measurements with a much more general stochastic measurement model. It integrates to Bayes’ rule over short time intervals and provides Bayes-optimal estimates from quantized, intermittent, or ambiguous sensor measurements. The filter has a close link to Information Theory, and we show that the rate of change of entropy of the density estimate is equal to the mutual information between the measurement and the state and thus the maximum achievable. This is a fundamentally new class of filter that is widely applicable to nonlinear estimation for continuous-time control.

Posted in: Bayesian filtering , Tagged:

Substituting the update step of a bayesian filter by a maximum likelihood optimisation in order to use non-linear observation models in a (linear-transition) Kalman framework

July 30, 2015 16:32 ,

Damián Marelli, Minyue Fu, and Brett Ninness, Asymptotic Optimality of the Maximum-Likelihood Kalman Filter for Bayesian Tracking With Multiple Nonlinear Sensors, IEEE Transactions on signal processing, vol. 63, no. 17, DOI: 10.1109/TSP.2015.2440220.

Bayesian tracking is a general technique for state estimation of nonlinear dynamic systems, but it suffers from the drawback of computational complexity. This paper is concerned with a class of Wiener systems with multiple nonlinear sensors. Such a system consists of a linear dynamic system followed by a set of static nonlinear measurements. We study a maximum-likelihood Kalman filtering (MLKF) technique which involves maximum-like-lihood estimation of the nonlinear measurements followed by classical Kalman filtering. This technique permits a distributed implementation of the Bayesian tracker and guarantees the boundedness of the estimation error. The focus of this paper is to study the extent to which the MLKF technique approximates the theoretically optimal Bayesian tracker. We provide conditions to guarantee that this approximation becomes asymptotically exact as the number of sensors becomes large. Two case studies are analyzed in detail.

Posted in: Bayesian filtering , Tagged: , , , ,

Quantum probability theory as an alternative to classical (Kolgomorov) probability theory for modelling human decision making processes, and a curious description of the effect of a particular ordering of decisions in the complete result

July 21, 2015 11:45 ,

Peter D. Bruza, Zheng Wang, Jerome R. Busemeyer, Quantum cognition: a new theoretical approach to psychology, Trends in Cognitive Sciences, Volume 19, Issue 7, July 2015, Pages 383-393, ISSN 1364-6613, DOI: 10.1016/j.tics.2015.05.001.

What type of probability theory best describes the way humans make judgments under uncertainty and decisions under conflict? Although rational models of cognition have become prominent and have achieved much success, they adhere to the laws of classical probability theory despite the fact that human reasoning does not always conform to these laws. For this reason we have seen the recent emergence of models based on an alternative probabilistic framework drawn from quantum theory. These quantum models show promise in addressing cognitive phenomena that have proven recalcitrant to modeling by means of classical probability theory. This review compares and contrasts probabilistic models based on Bayesian or classical versus quantum principles, and highlights the advantages and disadvantages of each approach.

Posted in: Probability theories and interpretations, Psycho-physiological bases of engineering , Tagged: , , ,

Transfer learning in reinforcement learning through case-based and the use of heuristics for selecting actions

July 21, 2015 11:24 ,

Reinaldo A.C. Bianchi, Luiz A. Celiberto Jr., Paulo E. Santos, Jackson P. Matsuura, Ramon Lopez de Mantaras, Transferring knowledge as heuristics in reinforcement learning: A case-based approach, Artificial Intelligence, Volume 226, September 2015, Pages 102-121, ISSN 0004-3702, DOI: 10.1016/j.artint.2015.05.008.

The goal of this paper is to propose and analyse a transfer learning meta-algorithm that allows the implementation of distinct methods using heuristics to accelerate a Reinforcement Learning procedure in one domain (the target) that are obtained from another (simpler) domain (the source domain). This meta-algorithm works in three stages: first, it uses a Reinforcement Learning step to learn a task on the source domain, storing the knowledge thus obtained in a case base; second, it does an unsupervised mapping of the source-domain actions to the target-domain actions; and, third, the case base obtained in the first stage is used as heuristics to speed up the learning process in the target domain.
A set of empirical evaluations were conducted in two target domains: the 3D mountain car (using a learned case base from a 2D simulation) and stability learning for a humanoid robot in the Robocup 3D Soccer Simulator (that uses knowledge learned from the Acrobot domain). The results attest that our transfer learning algorithm outperforms recent heuristically-accelerated reinforcement learning and transfer learning algorithms.

Posted in: Reinforcement learning in AI, Reinforcement learning theory , Tagged: , , , ,

Finding the common utility of actions in several tasks learnt in the same domain in order to reduce the learning cost of reinforcement learning

July 21, 2015 08:42 ,

Rosman, B.; Ramamoorthy, S., Action Priors for Learning Domain Invariances, Autonomous Mental Development, IEEE Transactions on , vol.7, no.2, pp.107,118, June 2015, DOI: 10.1109/TAMD.2015.2419715.

An agent tasked with solving a number of different decision making problems in similar environments has an opportunity to learn over a longer timescale than each individual task. Through examining solutions to different tasks, it can uncover behavioral invariances in the domain, by identifying actions to be prioritized in local contexts, invariant to task details. This information has the effect of greatly increasing the speed of solving new problems. We formalise this notion as action priors, defined as distributions over the action space, conditioned on environment state, and show how these can be learnt from a set of value functions. We apply action priors in the setting of reinforcement learning, to bias action selection during exploration. Aggressive use of action priors performs context based pruning of the available actions, thus reducing the complexity of lookahead during search. We additionally define action priors over observation features, rather than states, which provides further flexibility and generalizability, with the additional benefit of enabling feature selection. Action priors are demonstrated in experiments in a simulated factory environment and a large random graph domain, and show significant speed ups in learning new tasks. Furthermore, we argue that this mechanism is cognitively plausible, and is compatible with findings from cognitive psychology.

Posted in: Psycho-physiological bases of engineering, Reinforcement learning theory , Tagged: , , ,