Search Results - mathematical statistics data processing.

Process control : a practical approach by King, Myke, 1951-

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Introduction to imprecise probabilities

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Applied data analytics : principles and applications by Agbinya, Johnson I.

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MATLAB® for Neuroscientists : an introduction to scientific computing in MATLAB® by Wallisch, Pascal, 1978-

Table of Contents: “…MATLAB tutorial -- Mathematics and statistics tutorial -- Programming tutorial : principles and best practices -- Visualization and documentation tutorial -- Collecting reaction times I : visual search and pop out -- Collecting reaction times II : attention -- Psychophysics -- Psychophysics with GUIs -- Signal detection theory -- Frequency analysis part I : Fourier decomposition -- Frequency analysis part II : nonstationary signals and spectograms -- Wavelets -- Introduction to phase plane analysis -- Exploring the Figzhugh-Nagumo model -- Convolution -- Neural data analysis I : encoding -- Neural data analysis II : binned spike data -- Principal components analysis -- Information theory -- Neural decoding part I : discrete variables -- Neural decoding part II : continuous variables -- Local field potentials -- Functional magnetic imaging -- Voltage-gated ion channels -- Synaptic transmission -- Modeling a single neuron -- Models of the retina -- SImplified model of spiking neurons -- Fitzhugh-Nagumo model : traveling waves -- Decision theory lab -- Markov models -- Modeling spike trains as a Poisson process -- Exploring the Wilson-Cowan equations -- Neural networks as forest fires : stochastic neurodynamics -- Neural networks lab I : unsupervised learing -- Neural network lab II : supervised learning.…”
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Mathematics of evolution and phylogeny

Table of Contents: “….; 6.7 Other applications and perspectives -- 7 Phylogenetic networks -- 7.1 Introduction -- 7.2 Median networks -- 7.3 Visual complexity of median networks -- 7.4 Consensus networks -- 7.5 Treelikeness -- 7.6 Deriving phylogenetic networks from distances -- 7.7 Neighbour-net -- 7.8 Discussion -- Acknowledgements -- 8 Reconstructing the duplication history of tandemly repeated sequences -- 8.1 Introduction -- 8.2 Repeated sequences and duplication model -- 8.2.1 Di.erent categories of repeated sequences -- 8.2.2 Biological model and assumptions -- 8.2.3 Duplication events, duplication histories, and duplication trees -- 8.2.4 The human T-cell receptor Gamma genes -- 8.2.5 Other data sets, applicability of the model -- 8.3 Mathematical model and properties -- 8.3.1 Notation -- 8.3.2 Root position -- 8.3.3 Recursive de.nition of rooted and unrooted duplication trees -- 8.3.4 From phylogenies with ordered leaves to duplication trees.; 8.3.5 Topñdown approach and leftñright properties of rooted duplication trees -- 8.3.6 Counting duplication histories -- 8.3.7 Counting simple event duplication trees -- 8.3.8 Counting (unrestricted) duplication trees -- 8.4 Inferring duplication trees from sequence data -- 8.4.1 Preamble -- 8.4.2 Computational hardness of duplication tree inference -- 8.4.3 Distance-based inference of simple event duplication trees -- 8.4.4 A simple parsimony heuristic to infer unrestricted duplication trees -- 8.4.5 Simple distance-based heuristic to infer unrestricted duplication trees -- 8.5 Simulation comparison and prospects -- Acknowledgements -- 9 Conserved segment statistics and rearrangement inferences in comparative genomics -- 9.1 Introduction -- 9.2 Genetic (recombinational) distance -- 9.3 Gene counts -- 9.4 The inference problem -- 9.5 What can we infer from conserved segments? …”
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Global Health Risks : Mortality and Burden of Disease Attributable to Selected Major Risks. by Organization, World Health

Subjects: “…Medicine Data processing.…”
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Mathematics and life sciences

Table of Contents: “…1 Introduction; 1.1 Scientific Frontiers at the Interface of Mathematics and Life Sciences; 1.1.1 Developing the Language of Science and Its Interdisciplinary Character; 1.1.2 Challenges at the Interface: Mathematics and Life Sciences; 1.1.3 What This Book Is About; 1.1.4 Concluding Remarks; 2 Mathematical and Statistical Modeling of Biological Systems; 2.1 Ensemble Modeling of Biological Systems; 2.1.1 Introduction; 2.1.2 Background; 2.1.3 Ensemble Model; 2.1.4 Computational Techniques; 2.1.5 Application to Viral Infection Dynamics; 2.1.6 Ensemble Models in Biology; 2.1.7 Conclusions.…”
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Mathematical GeoEnergy : Oil Discovery, Depletion and Renewable Energy Analysis. by Pukite, Paul

Table of Contents: “…DISPERSIVE AGGREGATION MODEL OF RESERVOIR SIZES; 4.3. COMPARISON WITH REAL DATA…”
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Case Studies in Bayesian Statistical Modelling and Analysis.

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Handbook of environmental and ecological statistics by Fuentes, Montse, Hoeting, Jennifer A. (Jennifer Ann), 1966-, Smith, Richard L., 1953-

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Translating statistics to make decisions : a guide for the non-statistician by Cox, Victoria

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Statistical analysis in climate research by Storch, H. v. (Hans von), 1949-

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Simulation and inference for stochastic differential equations : with R examples by Iacus, Stefano M. (Stefano Maria)

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The Bioinformatics : Machine Learning Approach. by Brunak, Søren

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An introduction to probability and statistical inference by Roussas, George G.

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Understanding elections through statistics : polling, prediction, and testing by Forsberg, Ole J.

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EEG Signal Processing and Machine Learning. by Sanei, Saeid

Table of Contents: “…5.11 Separation of Correlated Sources via Tensor Factorization -- 5.12 Common Component Analysis -- 5.13 Canonical Correlation Analysis -- 5.14 Summary -- References -- Chapter 6 Chaos and Dynamical Analysis -- 6.1 Introduction to Chaos and Dynamical Systems -- 6.2 Entropy -- 6.3 Kolmogorov Entropy -- 6.4 Multiscale Fluctuation-Based Dispersion Entropy -- 6.5 Lyapunov Exponents -- 6.6 Plotting the Attractor Dimensions from Time Series -- 6.7 Estimation of Lyapunov Exponents from Time Series -- 6.7.1 Optimum Time Delay -- 6.7.2 Optimum Embedding Dimension -- 6.8 Approximate Entropy -- 6.9 Using Prediction Order -- 6.10 Summary -- References -- Chapter 7 Machine Learning for EEG Analysis -- 7.1 Introduction -- 7.2 Clustering Approaches -- 7.2.1 k-Means Clustering Algorithm -- 7.2.2 Iterative Self-Organizing Data Analysis Technique -- 7.2.3 Gap Statistics -- 7.2.4 Density-Based Clustering -- 7.2.5 Affinity-Based Clustering -- 7.2.6 Deep Clustering -- 7.2.7 Semi-Supervised Clustering -- 7.2.7.1 Basic Semi-Supervised Techniques -- 7.2.7.2 Deep Semi-Supervised Techniques -- 7.2.8 Fuzzy Clustering -- 7.3 Classification Algorithms -- 7.3.1 Decision Trees -- 7.3.2 Random Forest -- 7.3.3 Linear Discriminant Analysis -- 7.3.4 Support Vector Machines -- 7.3.5 k-Nearest Neighbour -- 7.3.6 Gaussian Mixture Model -- 7.3.7 Logistic Regression -- 7.3.8 Reinforcement Learning -- 7.3.9 Artificial Neural Networks -- 7.3.9.1 Deep Neural Networks -- 7.3.9.2 Convolutional Neural Networks -- 7.3.9.3 Autoencoders -- 7.3.9.4 Variational Autoencoder -- 7.3.9.5 Recent DNN Approaches -- 7.3.9.6 Spike Neural Networks -- 7.3.9.7 Applications of DNNs to EEG -- 7.3.10 Gaussian Processes -- 7.3.11 Neural Processes -- 7.3.12 Graph Convolutional Networks -- 7.3.13 Naïve Bayes Classifier -- 7.3.14 Hidden Markov Model -- 7.3.14.1 Forward Algorithm -- 7.3.14.2 Backward Algorithm.…”
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Digital Signal Processing : a Practical Guide for Engineers and Scientists. by Smith, Steven W.

Table of Contents: “…The Breadth and Depth of DSP; The Roots of DSP; Telecommunications; Audio Processing; Echo Location; Image Processing; Chapter 2. …”
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Advanced Mathematical and Computational Tools in Metrology Vii. by Ciarlini, P.

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Advanced Mathematical And Computational Tools In Metrology Vi. by Ciarlini, P.

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