Probability & Statistics
Probability & statistics centers on the mathematical study of uncertainty, chance, and data analysis—essential for quantifying variability and making informed predictions. This category covers foundational principles including probability theory, distributions, statistical inference, and hypothesis testing, often underpinning work in science, engineering, economics, and the social sciences.
To focus your reading or research, explore subtopics such as Bayesian analysis for updating beliefs, regression analysis to understand relationships between variables, or stochastic processes and time series for modeling random phenomena across time. These distinct areas help tailor your learning to specialized applications or theoretical interests within the wider field of mathematics.
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Nonlinear Mixture Models
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Everyday Probab & Statis, 2 Ed
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Intro to Stoch Calc with Appl, 3 Ed
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Simulating Copulas: Stoch Model, Sampl..
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Extreme Value Distributions
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Intro to Stoch Calc with Appl, 2 Ed
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Nonsmooth Optimization (B/H)
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Martingales & Stochastic Analysis (V1)
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Introduction to Asymptotics
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Principles of Stat Inference (V4)
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Statistics and Truth (2nd Ed)
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Nonsmooth Optimization
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Stochastic Pdes and Modelling of Multiscale Complex System
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Linear Models and Regression with R: An Integrated Approach
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Probability: Theory, Examples, Problems, Simulations: Examples
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Genealogies of Interacting Particle Systems
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Probability: Theory, Examples, Problems, Simulations: Examples
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A First Look at Stochastic Processes
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A First Look at Stochastic Processes
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Hilbert and Banach Space-Valued Stochastic Processes
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Universal Theory for Strong Limit Theorems of Probability
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Stoch Intere Rate Model (3rd Ed)
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Linear Models and Regression with R: An Integrated Approach
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