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

1980 Preface * 1999 Preface * 1999 Acknowledgements * Introduction * 1 Circular Logic * 2 Phase Singularities (Screwy Results of Circular Logic) * 3 The Rules of the Ring * 4 Ring Populations * 5 Getting Off the Ring * 6 Attracting Cycles and Isochrons * 7 Measuring the Trajectories of a Circadian Clock * 8 Populations of Attractor Cycle Oscillators * 9 Excitable Kinetics and Excitable Media * 10 The Varieties of Phaseless Experience: In Which the Geometrical Orderliness of Rhythmic Organization Breaks Down in Diverse Ways * 11 The Firefly Machine 12 Energy Metabolism in Cells * 13 The Malonic Acid Reagent ('Sodium Geometrate') * 14 Electrical Rhythmicity and Excitability in Cell Membranes * 15 The Aggregation of Slime Mold Amoebae * 16 Numerical Organizing Centers * 17 Electrical Singular Filaments in the Heart Wall * 18 Pattern Formation in the Fungi * 19 Circadian Rhythms in General * 20 The Circadian Clocks of Insect Eclosion * 21 The Flower of Kalanchoe * 22 The Cell Mitotic Cycle * 23 The Female Cycle * References * Index of Names * Index of Subjects

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/596B270197FE27DE5FABB598B6210622/S0025557200150892a.pdf/div-class-title-span-class-italic-the-geometry-of-biological-time-span-by-a-t-winfree-pp-544-dm68-corrected-second-printing-1990-isbn-3-540-52528-9-springer-div.pdf

The geometry of biological time , by A. T. Winfree. Pp 544. DM68. Corrected Second Printing 1990. ISBN 3-540-52528-9 (Springer)

An Introduction to MultiAgent Systems.

中枢神経系疾患の治療は正常細胞（ニューロン）の機能維持を目的とするが，脳血管障害のように機能障害の原因が細胞の死滅に基づくことは多い．一方，脳腫瘍の治療においては薬物療法や放射線療法といった腫瘍細胞の死滅を目標とするものが大きな位置を占める．いずれの場合にも，細胞死の機序を理解することは各種病態や治療法の理解のうえで重要である．現在のところ最も研究の進んでいる細胞死の型はアポトーシスである．そのなかで重要な位置を占めるミトコンドリアにおける反応および抗アポトーシス因子について概要を紹介する．

Neuroscience 細胞死：最近の知見

where A represents the magnetic vector potential, is an integral of the hydromagnetic equations. This -integral made it possible to formulate a variational principle for the force-free magnetic fields. The integral expresses the fact that motions cannot transform a given field in an entirely arbitrary different field, if the conductivity of the medium isconsidered infinite. In this paper we shall show that the full set of hydromagnetic equations admit five more integrals, besides the energy integral, if dissipative processes are absent. These integrals, as we shall presently verify, are I2 =fbHvdV, (2)

Proceedings of the National Academy of Sciences

In this paper, we offer a gentle introduction to Gaussian processes for time-series data analysis. The conceptual framework of Bayesian modelling for time-series data is discussed and the foundations of Bayesian non-parametric modelling presented for Gaussian processes . We discuss how domain knowledge influences design of the Gaussian process models and provide case examples to highlight the approaches.

/pdf/gaussian-processes-for-time-series-modelling-216o4pnfir.pdf

Gaussian processes for time-series modelling

Identifying overlapping communities in networks is a challenging task. In this work we present a probabilistic approach to community detection that utilizes a Bayesian non-negative matrix factorization model to extract overlapping modules from a network. The scheme has the advantage of soft-partitioning solutions, assignment of node participation scores to modules, and an intuitive foundation. We present the performance of the method against a variety of benchmark problems and compare and contrast it to several other algorithms for community detection.

https://www.robots.ox.ac.uk/~sjrob/Pubs/PRE_NMF_finalProof.pdf

Overlapping community detection using Bayesian non-negative matrix factorization

Figure 1 illustrates a typical example of a prediction problem: given some noisy observations of a dependent variable at certain values of the independent variable , what is our best estimate of the dependent variable at a new value, ? If we expect the underlying function to be linear, and can make some assumptions about the input data, we might use a least-squares method to fit a straight line (linear regression). Moreover, if we suspect may also be quadratic, cubic, or even nonpolynomial, we can use the principles of model selection to choose among the various possibilities. Gaussian process regression (GPR) is an even finer approach than this. Rather than claiming relates to some specific models (e.g. ), a Gaussian process can represent obliquely, but rigorously, by letting the data ‘speak’ more clearly for themselves. GPR is still a form of supervised learning, but the training data are harnessed in a subtler way. As such, GPR is a less ‘parametric’ tool. However, it’s not completely free-form, and if we’re unwilling to make even basic assumptions about , then more general techniques should be considered, including those underpinned by the principle of maximum entropy; Chapter 6 of Sivia and Skilling (2006) offers an introduction.

/pdf/gaussian-processes-for-regression-a-quick-introduction-3oi7jyl4ef.pdf

Gaussian Processes for Regression: A Quick Introduction

Wavelet cross-correlation (WCC) is used to analyse the relationship between low-frequency oscillations in near-infrared spectroscopy (NIRS) measured cerebral oxyhaemoglobin (O2Hb) and mean arterial blood pressure (MAP) in patients suffering from autonomic failure and age-matched controls. Statistically significant differences are found in the wavelet scale of maximum cross-correlation upon posture change in patients, but not in controls. We propose that WCC analysis of the relationship between O2Hb and MAP provides a useful method of investigating the dynamics of cerebral autoregulation using the spontaneous low-frequency oscillations that are typically observed in both variables without having to make the assumption of stationarity of the time series. It is suggested that for a short-duration clinical test previous transfer-function-based approaches to analyse this relationship may suffer due to the inherent nonstationarity of low-frequency oscillations that are observed in the resting brain.

/pdf/synchronization-between-arterial-blood-pressure-and-cerebral-5gt3qcs6rf.pdf

Synchronization between arterial blood pressure and cerebral oxyhaemoglobin concentration investigated by wavelet cross-correlation

A gentle introduction to Gaussian processes (GPs). The three parts of the document consider GPs for regression, classification, and dimensionality reduction.

Mark Ebden

Papers

Gaussian processes for time-series modelling

Overlapping community detection using Bayesian non-negative matrix factorization

Gaussian Processes for Regression: A Quick Introduction

Synchronization between arterial blood pressure and cerebral oxyhaemoglobin concentration investigated by wavelet cross-correlation

Gaussian Processes: A Quick Introduction