تبدیل گچ به سیمان | مقالات مهندسی عمران

A defense of Columbo: A multilevel introduction to probabilistic reasoning
by G. D'Agostini - arXiv , 2010
Triggered by a recent interesting article on the too frequent incorrect use of probabilistic evidence in courts, the author introduces the basic concepts of probabilistic inference with a toy model, and discusses several important issues.

An Introduction to Statistical Signal Processing
by R. M. Gray, L. D. Davisson - Cambridge University Press , 2005
The book covers basic probability, random objects, expectation, second order moment theory with examples of the random process models and their basic properties, specific applications for communication, estimation, detection, modulation.

An Introduction to Stochastic PDEs
by Martin Hairer - arXiv , 2009
This text is an attempt to give a reasonably self-contained presentation of the basic theory of stochastic partial differential equations, taking for granted basic measure theory, functional analysis and probability theory, but nothing else.

Applied Nonparametric Regression
by Wolfgang Härdle - Cambridge University Press , 1992
Nonparametric regression analysis has become central to economic theory. Hardle, by writing the first comprehensive and accessible book on the subject, contributed enormously to making nonparametric regression equally central to econometric practice.

Bayesian Spectrum Analysis and Parameter Estimation
by G. Larry Bretthorst - Springer , 1988
This work is a research document on the application of probability theory to the parameter estimation problem. The people who will be interested in this material are physicists, economists, and engineers who have to deal with data on a daily basis.

Convergence of Stochastic Processes
by D. Pollard - Springer , 1984
Selected parts of empirical process theory, with applications to mathematical statistics. The book describes the combinatorial ideas needed to prove maximal inequalities for empirical processes indexed by classes of sets or classes of functions.

Correlation and Causality
by David A. Kenny - John Wiley & Sons Inc , 1979
This text is a general introduction to the topic of structural analysis. It presumes no previous acquaintance with causal analysis. It is general because it covers all the standard, as well as a few nonstandard, statistical procedures.

Design of Comparative Experiments
by R. A. Bailey - Cambridge University Press , 2008
This book develops a coherent framework for thinking about factors that affect experiments and their relationships, including the use of Hasse diagrams. The book is ideal for advanced undergraduate and beginning graduate courses.

generatingfunctionology
by Herbert S. Wilf - A K Peters, Ltd. , 2006
The book about main ideas on generating functions and some of their uses in discrete mathematics. Generating functions are a bridge between discrete mathematics and continuous analysis. The book is suitable for undergraduates.

Introduction to Randomness and Statistics
by Alexander K. Hartmann - arXiv , 2009
This is a practical introduction to randomness and data analysis, in particular in the context of computer simulations. At the beginning, the most basics concepts of probability are given, in particular discrete and continuous random variables.

Inverse Problem Theory and Methods for Model Parameter Estimation
by Albert Tarantola - SIAM , 2004
The first part deals with discrete inverse problems with a finite number of parameters, while the second part deals with general inverse problems. The book for scientists and applied mathematicians facing the interpretation of experimental data.

Lectures on Probability, Statistics and Econometrics
by Marco Taboga - statlect.com , 2010
This e-book is organized as a website that provides access to a series of lectures on fundamentals of probability, statistics and econometrics, as well as to a number of exercises on the same topics. The level is intermediate.

Lectures on Stochastic Analysis
by Thomas G. Kurtz - University of Wisconsin , 2007
Covered topics: stochastic integrals with respect to general semimartingales, stochastic differential equations based on these integrals, integration with respect to Poisson measures, stochastic differential equations for general Markov processes.

Markov Chains and Mixing Times
by D. A. Levin, Y. Peres, E. L. Wilmer - American Mathematical Society , 2008
An introduction to the modern approach to the theory of Markov chains. The main goal of this approach is to determine the rate of convergence of a Markov chain to the stationary distribution as a function of the size and geometry of the state space.

Markov Chains and Stochastic Stability
by S.P. Meyn, R.L. Tweedie - Springer , 2005
The book on the theory of general state space Markov chains, and its application to time series analysis, operations research and systems and control theory. An advanced graduate text and a monograph treating the stability of Markov chains.

Non-Uniform Random Variate Generation
by Luc Devroye - Springer , 1986
The book on small field on the crossroads of statistics, operations research and computer science. The applications of random number generators are wide and varied. The study of non-uniform random variates is precisely the subject area of the book.

Principles of Data Analysis
by Cappella Archive - Prasenjit Saha , 2003
This is a short book about the principles of data analysis. The emphasis is on why things are done rather than on exactly how to do them. If you already know something about the subject, then working through this book will deepen your understanding.

Probability and Statistics in Engineering
by Daniele Veneziano - MIT OpenCourseWare , 2005
This class covers quantitative analysis of uncertainty and risk for engineering applications. Fundamentals of probability, random processes, statistics, and decision analysis are covered, along with random variables and vectors.

Random Matrix Models and Their Applications
by Pavel Bleher, Alexander Its - Cambridge University Press , 2001
The book covers broad areas such as topologic and combinatorial aspects of random matrix theory; scaling limits, universalities and phase transitions in matrix models; universalities for random polynomials; and applications to integrable systems.

Reversibility and Stochastic Networks
by F.P. Kelly - John Wiley and Sons Ltd , 1979
The book on vector stochastic processes in equilibrium or stochastic networks, with wide range of applications. It covers the concept of reversibility, the output from a queue, and the epolymerization process quilibrium distribution.

Reversible Markov Chains and Random Walks on Graphs
by Aldous, Fill - University of California, Berkeley , 2002
From the table of contents: General Markov Chains; Reversible Markov Chains; Hitting and Convergence Time, and Flow Rate, Parameters for Reversible Markov Chains; Special Graphs and Trees; Cover Times; Symmetric Graphs and Chains; etc.

Statistics, Probability, and Game Theory: papers in honor of David Blackwell
by David Blackwell, at al. - IMS , 1996
The bulk of the articles in this volume are research articles in probability, statistics, gambling, game theory, Markov decision processes, set theory and logic, comparison of experiments, games of timing, merging of opinions, etc.

Stochastic Analysis - Notes
by I. F. Wilde , 2009
A gentle introduction to the mathematics of Stochastic Analysis. From the table of contents: Introduction; Conditional expectation; Martingales; Stochastic integration - informally; Wiener process; Ito's formula; Bibliography.

Stochastic Calculus
by Alan Bain , 2008
An informal introduction to Stochastic Calculus, and especially to the Ito integral and some of its applications. The text concentrates on the parts of the course which the author found hard, there is little or no comment on more standard matters.

Stochastic Integration and Stochastic Differential Equations
by Klaus Bichteler - University of Texas , 2002
Written for graduate students of mathematics, physics, electrical engineering, and finance. The students are expected to know the basics of point set topology up to Tychonoff's theorem, general integration theory, and some functional analysis.