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Time Series Modeling of Neuroscience Data

Recent advances in brain science measurement technology have given researchers access to very large-scale time series data such as EEG/MEG;

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Stevens' Handbook of Experimental Psychology

; network models for clinical psychology; response time modeling; analyzing neural time series data; models and methods for reinforcement learning;

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Machine Learning for Time Series Forecasting with Python

concepts, such as stationarity, horizon, trend, and seasonality Prepare time series data for modeling Evaluate time series forecasting models;

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Statistical Methods for Modeling Human Dynamics

science, and physics. State-of-the-art techniques and applications used to analyze data obtained from studies in cognition, emotion, and;

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Statistical Techniques for Neuroscientists

Statistical Techniques for Neuroscientists introduces new and useful methods for data analysis involving simultaneous recording of neuron;

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Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling

novel contributions to the analysis of time series and panel data with applications in macroeconomics, finance, cognitive science and psychology;

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Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling

novel contributions to the analysis of time series and panel data with applications in macroeconomics, finance, cognitive science and psychology;

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Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data

Analysis for Longitudinal and Time-Series Image Data, STIA 2014, held in conjunction with MICCAI 2014 in Boston, MA, USA, in September 2014. The 7;

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Time Series in Economics and Finance

This book presents the principles and methods for the practical analysis and prediction of economic and financial time series. It covers;

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Time Series in Economics and Finance

This book presents the principles and methods for the practical analysis and prediction of economic and financial time series. It covers;

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Time Series Algorithms Recipes

-solution-style recipes, starting with data reading and preprocessing. It begins with the fundamentals of time series forecasting using statistical;

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Handbook of Discrete-Valued Time Series

series of counts and incorporates frequentist and Bayesian approaches for discrete-valued spatio-temporal data and multivariate data. While the;

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Handbook of Discrete-Valued Time Series

series of counts and incorporates frequentist and Bayesian approaches for discrete-valued spatio-temporal data and multivariate data. While the;

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Applied Bayesian Forecasting and Time Series Analysis

dynamic linear mode (DLM). They include a complete theoretical development of the DLM and illustrate each step with analysis of time series data;

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Applied Bayesian Forecasting and Time Series Analysis

dynamic linear mode (DLM). They include a complete theoretical development of the DLM and illustrate each step with analysis of time series data;

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Time Series Analysis

details and purpose of a variety of univariate and multivariate time series methods. Numerous figures, tables and real-life time series data sets;

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Time Series

Focusing on Bayesian approaches and computations using simulation-based methods for inference, Time Series: Modeling, Computation, and;

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Data Analysis and Applications 1

This series of books collects a diverse array of work that provides the reader with theoretical and applied information on data analysis;

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An Introduction to Model-Based Cognitive Neuroscience

Two recent innovations, the emergence of formal cognitive models and the addition of cognitive neuroscience data to the traditional;

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Time Series Analysis

-life time series data sets. The book also introduces univariate and multivariate time series models and methods which are useful for analyzing;

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Applied Time Series

data series. The result is clear presentation, quantified subjective judgment derived from selected models applied to time series observations;

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Models for Dependent Time Series

, applicable, and statistically valid conclusions from multivariate (or vector) time series data. The first four chapters discuss the two main;

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Models for Dependent Time Series

, applicable, and statistically valid conclusions from multivariate (or vector) time series data. The first four chapters discuss the two main;

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Practical Time Series Forecasting

series forecasting capabilities can be used with the book. Galit Shmueli is the SRITNE Chaired Professor of Data Analytics at the Indian School;

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Introduction to Time Series Modeling

In time series modeling, the behavior of a certain phenomenon is expressed in relation to the past values of itself and other covariates;

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Mathematical Modelling in Motor Neuroscience: State of the Art and Translation to the Clinic. Ocular Motor Plant and Gaze Stabilization Mechanisms

Mechanisms, Volume 248, the latest release in the Progress in Brain Research series, highlights new advances in the field, with this new volume;

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Time Series and Spatial Analysis of Crop Yield

Space and time are often vital components of research data sets. Accounting for and utilizing the space and time information in statistical;

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