Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Fundamentals Of Statistical Signal Processing

Monte Carlo methods are revolutionizing the on-line analysis of data in fields as diverse as financial modeling, target tracking and computer vision. These methods, appearing under the names of bootstrap filters, condensation, optimal Monte Carlo filters, particle filters and survival of the fittest, have made it possible to solve numerically many complex, non-standard problems that were previously intractable. This book presents the first comprehensive treatment of these techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modeling, neural networks, optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection, computer vision, semiconductor design, population biology, dynamic Bayesian networks, and time series analysis. This will be of great value to students, researchers and practitioners, who have some basic knowledge of probability. Arnaud Doucet received the Ph. D. degree from the University of Paris-XI Orsay in 1997. From 1998 to 2000, he conducted research at the Signal Processing Group of Cambridge University, UK. He is currently an assistant professor at the Department of Electrical Engineering of Melbourne University, Australia. His research interests include Bayesian statistics, dynamic models and Monte Carlo methods. Nando de Freitas obtained a Ph.D. degree in information engineering from Cambridge University in 1999. He is presently a research associate with the artificial intelligence group of the University of California at Berkeley. His main research interests are in Bayesian statistics and the application of on-line and batch Monte Carlo methods to machine learning. Neil Gordon obtained a Ph.D. in Statistics from Imperial College, University of London in 1993. He is with the Pattern and Information Processing group at the Defence Evaluation and Research Agency in the United Kingdom. His research interests are in time series, statistical data analysis, and pattern recognition with a particular emphasis on target tracking and missile guidance.

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Sequential Monte Carlo methods in practice

物件導向軟體之架構(Object-Oriented Software Construction)探討

This paper presents an overview of progress in the area of multiple input multiple output (MIMO) space-time coded wireless systems. After some background on the research leading to the discovery of the enormous potential of MIMO wireless links, we highlight the different classes of techniques and algorithms proposed which attempt to realize the various benefits of MIMO including spatial multiplexing and space-time coding schemes. These algorithms are often derived and analyzed under ideal independent fading conditions. We present the state of the art in channel modeling and measurements, leading to a better understanding of actual MIMO gains. Finally, the paper addresses current questions regarding the integration of MIMO links in practical wireless systems and standards.

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From theory to practice: an overview of MIMO space-time coded wireless systems

Synthesis of Synchronization Algorithms

This paper focuses on a main domain for core based DSP systems. In wireless applications it is an indispensable requirement to optimally utilize computational power and on-chip memory. It is well known that to achieve this goal, DSPs are almost exclusively programmed in assembly, due to the poor efficiency of the existing compilers. In view of the required drastic increase of the designer's productivity and of design reuse it is evident that the single most important contribution boosting efficiency will come from high-level language support for DSP applications.

On core and more: a design perspective for system-on-chip

We provide an information theoretic analysis of Wald's sequential probability ratio test. The optimality of the Wald test in the sense that it yields the minimum average decision time for a binary decision problem is reflected by the evolution of the information densities over time. Information densities are considered as they take into account the fact that the termination time of the Wald test depends on the actual realization of the observation sequence. Based on information densities we show that in case the test terminates at time instant $k$ the probability to decide for hypothesis $\mathcal{H}_1$ (or the counter-hypothesis $\mathcal{H}_0$) is independent of time. We use this characteristic to evaluate the evolution of the mutual information between the binary random variable and the decision variable of the Wald test. Our results establish a connection between minimum mean decision times and the corresponding information processing.

Information Density and Optimality of Sequential Decision-Making

Timing Adjustment by Interpolation

The design complexity of an ASIC as well as its hardware expenditure can be optimized if software is used as far as possible and application-specific hardware executes only the critical algorithms. Experience within a chipset design project has led to various criteria to evaluate the tradeoff between hardware and software. Partitioning under great uncertainty concerning many realization parameters is demonstrated. The gap in abstraction between specification and implementation is much greater in hardware design than in software development. The concepts applied in surmounting the gap, like abstract modeling, type of model, and hierarchy, are defined and their relations are examined. >

Heinrich Meyr

Papers

Synthesis of Synchronization Algorithms

On core and more: a design perspective for system-on-chip

Information Density and Optimality of Sequential Decision-Making

Timing Adjustment by Interpolation

Partitioning and surmounting the software-hardware abstraction gap in an ASIC design project