There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

This paper proposes a very tractable approach to modeling changes in regime. The parameters of an autoregression are viewed as the outcome of a discrete-state Markov process. For example, the mean growth rate of a nonstationary series may be subject to occasional, discrete shifts. The econometrician is presumed not to observe these shifts directly, but instead must draw probabilistic inference about whether and when they may have occurred based on the observed behavior of the series. The paper presents an algorithm for drawing such probabilistic inference in the form of a nonlinear iterative filter

A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle.

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Machine Learning : A Probabilistic Perspective

Forecasting with artificial neural networks: the state of the art

Recurrence plots for the analysis of complex systems

In this article, we present two nonparametric trispectrum based tests for testing the hypothesis that an observed time series was generated by what we call a generalized Wiener process (GWP). Assuming the existence of a Weiner process for asset rates of return is critical to the Black-Scholes model and its extension by Merton (BSM). The Hinich trispectrum-based test of linearity and the trispectrum extension of the Hinich-Rothman bispectrum test for time reversibility are used to test the validity of BSM. We apply the tests to a selection of high frequency NYSE and Australian (ASX) stocks.

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Testing for the Existence of a Generalized Wiener Process- the Case of Stock Prices

Mancur Lloyd Olson (1932–1998)

THE LOCATION OF AMERICAN PRESIDENTIAL CANDIDATES: AN EMPIRICAL TEST OF A NEW SPATIAL MODEL OF ELECTIONS††Delivered at the 1984 Annual Meeting of the American Political Science Association, Washington, D.C.

Melvin J. Hinich

Papers

Testing for the Existence of a Generalized Wiener Process- the Case of Stock Prices

Mancur Lloyd Olson (1932–1998)

THE LOCATION OF AMERICAN PRESIDENTIAL CANDIDATES: AN EMPIRICAL TEST OF A NEW SPATIAL MODEL OF ELECTIONS††Delivered at the 1984 Annual Meeting of the American Political Science Association, Washington, D.C.