Machine learning methods extract value from vast data sets quickly and with modest resources They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly People who know the methods have their choice of rewarding jobs This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world Numerous examples and exercises, both computer based and theoretical, are included in every chapter Resources for students and instructors, including a MATLAB toolbox, are available online

/pdf/bayesian-reasoning-and-machine-learning-276lqrjv6g.pdf

Bayesian Reasoning and Machine Learning

Adopted values for the reduced electric quadrupole transition probability, B(E2)↑, from the ground state to the first-excited 2+ state of even–even nuclides are given in Table I. Values of τ, the mean life of the 2+ state; E, the energy; and β, the quadrupole deformation parameter, are also listed there. The ratio of β to the value expected from the single-particle model is presented. The intrinsic quadrupole moment, Q0, is deduced from the B(E2)↑ value. The product E×B(E2)↑ is expressed as a percentage of the energy-weighted total and isoscalar E2 sum-rule strengths.

Table II presents the data on which Table I is based, namely the experimental results for B(E2)↑ values with quoted uncertainties. Information is also given on the quantity measured and the method used. The literature has been covered to November 2000.

The adopted B(E2)↑ values are compared in Table III with the values given by systematics and by various theoretical models. Predictions of unmeasured B(E2)↑ values are also given in Table III.

Transition probability from the ground to the first-excited 2^+ state of even-even nuclides

Soft amplitudes in hot gauge theories: A general analysis

We examine tests for jumps based on recent asymptotic results; we interpret the tests as Hausman-type tests. Monte Carlo evidence suggests that the daily ratio z-statistic has appropriate size, good power, and good jump detection capabilities revealed by the confusion matrix comprised of jump classification probabilities. We identify a pitfall in applying the asymptotic approximation over an entire sample. Theoretical and Monte Carlo analysis indicates that microstructure noise biases the tests against detecting jumps, and that a simple lagging strategy corrects the bias. Empirical work documents evidence for jumps that account for seven percent of stock market price variance.

/pdf/the-relative-contribution-of-jumps-to-total-price-variance-8runjpez0w.pdf

The Relative Contribution of Jumps to Total Price Variance

Physiological and behavioural phenomena are often complex, characterized by variations in time series. Variations in time series reflect how these phenomena organize into coherent structures by interactions that span multiple scales in both time and space. The present tutorial is an introduction to multifractal analyses that can identify these scale invariant interactions within time series by its multifractal spectrum. The multifractal spectrum can be estimated directly from scale-dependent measurements or from its q-order statistics. The tutorial emphasizes the most common scale-dependent measurements defined by the wavelet transforms and the detrended fluctuation analyses. The tutorial also emphasizes common features of all multifractal analyses, like the choice of linear regression method, scaling range and elimination of spurious singularities, which are important for a robust estimation of the multifractal spectrum. The tutorial ends with two brief examples where multifractal analyses are employed to time series from multifractal models and the complex phenomena of cognitive performance. References to available software for multifractal analyses are included at the end of the tutorial. The main aim of the tutorial is to give the reader an introduction to multifractal analyses without the extensive technicalities typically provided in mathematical journals.

https://www.ntnu.edu/documents/170234/1315232/Introduction_to_MFDFA.pdf

Introduction to Multifractal Detrended Fluctuation Analysis in Matlab

Financial time series, in general, exhibit average behaviour at “long” time scales and stochastic behaviour at ‘short” time scales As in statistical physics, the two have to be modelled using different approaches — deterministic for trends and probabilistic for fluctuations about the trend In this talk, we will describe a new wavelet based approach to separate the trend from the fluctuations in a time series A deterministic (non-linear regression) model is then constructed for the trend using genetic algorithm We thereby obtain an explicit analytic model to describe dynamics of the trend Further the model is used to make predictions of the trend We also study statistical and scaling properties of the fluctuations The fluctuations have non-Gaussian probability distribution function and show multi-scaling behaviour Thus, our work results in a comprehensive model of trends and fluctuations of a financial time series

Modelling Financial Time Series

A conformal scalar dyon black hole solution

Gravitational energy of a stringy charged black hole

We propose a wavelet based method for the characterization of the scaling behavior of nonstationary time series. It makes use of the built-in ability of the wavelets for capturing the trends in a data set, in variable window sizes. Discrete wavelets from the Daubechies family are used to illustrate the efficacy of this procedure. After studying binomial multifractal time series with the present and earlier approaches of detrending for comparison, we analyze the time series of averaged spin density in the 2D Ising model at the critical temperature, along with several experimental data sets possessing multifractal behavior.

https://www.prl.res.in/~library/Panigrahi_PK_2005_abst.pdf

Wavelet analysis and scaling properties of time series.

We illustrate the efficacy of a discrete wavelet based approach to characterize fluctuations in non-stationary time series. The present approach complements the multifractal detrended fluctuation analysis (MF-DFA) method and is quite accurate for small size data sets. As compared to polynomial fits in the MF-DFA, a single Daubechies wavelet is used here for detrending purposes. The natural, built-in variable window size in wavelet transforms makes this procedure well suited for non-stationary data. We illustrate the working of this method through the analysis of binomial multifractal model. For this model, our results compare well with those calculated analytically and obtained numerically through MF-DFA. To show the efficacy of this approach for finite data sets, we also do the above comparison for Gaussian white noise time series of different sizes. In addition, we analyze time series of three experimental data sets of tokamak plasma and also spin density fluctuations in 2D Ising model.

Jitendra C. Parikh

Papers

Modelling Financial Time Series

A conformal scalar dyon black hole solution

Gravitational energy of a stringy charged black hole

Wavelet analysis and scaling properties of time series.

Multiresolution analysis of fluctuations in non-stationary time series through discrete wavelets