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Courseware
The lectures are at a beginning graduate level and assume only basic familiarity with Functional Analysis and Probability Theory. Topics covered include: Random variables in Banach spaces: Gaussian random variables, contraction principles, Kahane-Khintchine inequality, Anderson’s inequality. Stochastic integration in Banach spaces I: γ-Radonifying operators, γ-boundedness, Brownian motion, Wiener stochastic integral. Stochastic evolution equations I: Linear stochastic evolution equations: existence and uniqueness, Hölder regularity. Stochastic integral in Banach spaces II: UMD spaces, decoupling inequalities, Itô stochastic integral. Stochastic evolution equations II: Nonlinear stochastic evolution equations: existence and uniqueness, Hölder regularity.
- Subjects:
- Mathematics and Statistics
- Keywords:
- Stochastic partial differential equations Evolution equations
- Resource Type:
- Courseware
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Courseware
Provides students with the basic tools for analyzing experimental data, properly interpreting statistical reports in the literature, and reasoning under uncertain situations. Topics organized around three key theories: Probability, statistical, and the linear model. Probability theory covers axioms of probability, discrete and continuous probability models, law of large numbers, and the Central Limit Theorem. Statistical theory covers estimation, likelihood theory, Bayesian methods, bootstrap and other Monte Carlo methods, as well as hypothesis testing, confidence intervals, elementary design of experiments principles and goodness-of-fit. The linear model theory covers the simple regression model and the analysis of variance. Places equal emphasis on theory, data analyses, and simulation studies.
- Subjects:
- Mathematics and Statistics and Biology
- Keywords:
- Statistics Cognitive science
- Resource Type:
- Courseware
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Courseware
This course offers an in-depth the theoretical foundations for statistical methods that are useful in many applications. The goal is to understand the role of mathematics in the research and development of efficient statistical methods.
- Subjects:
- Mathematics and Statistics
- Keywords:
- Mathematical statistics
- Resource Type:
- Courseware
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Courseware
Statistics is the science that turns data into information and information into knowledge. This class covers applied statistical methodology from an analysis-of-data viewpoint. Topics covered include frequency distributions; measures of location; mean, median, mode; measures of dispersion; variance; graphic presentation; elementary probability; populations and samples; sampling distributions; one sample univariate inference problems, and two sample problems; categorical data; regression and correlation; and analysis of variance. Use of computers in data analysis is also explored.
- Subjects:
- Mathematics and Statistics
- Keywords:
- Statistics
- Resource Type:
- Courseware
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Courseware
Mathematica and its applications to linear algebra, differential equations, and complex functions. Fourier series and Fourier transforms. Other topics in integral transforms.
- Subjects:
- Physics and Mathematics and Statistics
- Keywords:
- Physics Mathematical physics
- Resource Type:
- Courseware
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Courseware
This course is the continuation of 18.785 Number Theory I. It begins with an analysis of the quadratic case of Class Field Theory via Hilbert symbols, in order to give a more hands-on introduction to the ideas of Class Field Theory. More advanced topics in number theory are discussed in this course, such as Galois cohomology, proofs of class field theory, modular forms and automorphic forms, Galois representations, and quadratic forms.
- Subjects:
- Mathematics and Statistics
- Keywords:
- Galois cohomology Algebraic number theory Class field theory
- Resource Type:
- Courseware
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Courseware
This is the first semester of a one year graduate course in number theory covering standard topics in algebraic and analytic number theory. At various points in the course, we will make reference to material from other branches of mathematics, including topology, complex analysis, representation theory, and algebraic geometry.
- Subjects:
- Mathematics and Statistics
- Keywords:
- Number theory Algebraic number theory
- Resource Type:
- Courseware
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Courseware
This course provides students with decision theory, estimation, confidence intervals, and hypothesis testing. It introduces large sample theory, asymptotic efficiency of estimates, exponential families, and sequential analysis.
- Subjects:
- Mathematics and Statistics
- Keywords:
- Mathematical statistics
- Resource Type:
- Courseware
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Courseware
How do populations grow? How do viruses spread? What is the trajectory of a glider? Many real-life problems can be described and solved by mathematical models. In this course, you will form a team with another student and work in a project to solve a real-life problem. You will learn to analyze your chosen problem, formulate it as a mathematical model (containing ordinary differential equations), solve the equations in the model, and validate your results. You will learn how to implement Euler’s method in a Python program. If needed, you can refine or improve your model, based on your first results. Finally, you will learn how to report your findings in a scientific way. This course is mainly aimed at Bachelor students from Mathematics, Engineering and Science disciplines. However it will suit anyone who would like to learn how mathematical modeling can solve real-world problems.
- Subjects:
- Mathematics and Statistics
- Keywords:
- Mathematical models
- Resource Type:
- Courseware
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Courseware
In this course, students will learn basic linear algebra necessary to understand the operations regarding derivatives of functions with more than one variable to investigate maximum and minimum values of those functions with economics applications in mind. Students will also see how to solve linear systems and then how to turn them into problems involving matrices, then learn some of the important properties of matrices. This course will focus on topics in linear algebra and multivariable differential calculus suitable for economic applications. Recorded Summer 2013
- Subjects:
- economic applications, matrices, Economics, and Mathematics and Statistics
- Keywords:
- Economics Mathematical
- Resource Type:
- Courseware
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