Skip to main content Accessibility help
×
  1. Home
  2. Browse subjects
  3. Statistics and Probability
  4. General statistics and probability
  5. Content listing

Refine search

Refine search

Access:
Content type:
Publication date:
Apply   From and To filters
Subject: Show more
Journals Show more
Publishers: Show more
Societies Show more
Series: Show more
Collections: Show more

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Save to Kindle
  • Save to Dropbox
  • Save to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
Cancel
×

2703 results in General statistics and probability

Page 33 of 136

Applied Stochastic Differential Equations
  • Simo Särkkä, Arno Solin
  • Published online:
    16 April 2019
    Print publication:
    02 May 2019
  • Stochastic differential equations are differential equations whose solutions are stochastic processes. They exhibit appealing mathematical properties that are useful in modeling uncertainties and noisy phenomena in many disciplines. This book is motivated by applications of stochastic differential equations in target tracking and medical technology and, in particular, their use in methodologies such as filtering, smoothing, parameter estimation, and machine learning. It builds an intuitive hands-on understanding of what stochastic differential equations are all about, but also covers the essentials of Itô calculus, the central theorems in the field, and such approximation schemes as stochastic Runge–Kutta. Greater emphasis is given to solution methods than to analysis of theoretical properties of the equations. The book's practical approach assumes only prior understanding of ordinary differential equations. The numerous worked examples and end-of-chapter exercises include application-driven derivations and computational assignments. MATLAB/Octave source code is available for download, promoting hands-on work with the methods.

Probability
  • Theory and Examples
  • 5th edition
  • Rick Durrett
  • Published online:
    05 April 2019
    Print publication:
    18 April 2019
  • This lively introduction to measure-theoretic probability theory covers laws of large numbers, central limit theorems, random walks, martingales, Markov chains, ergodic theorems, and Brownian motion. Concentrating on results that are the most useful for applications, this comprehensive treatment is a rigorous graduate text and reference. Operating under the philosophy that the best way to learn probability is to see it in action, the book contains extended examples that apply the theory to concrete applications. This fifth edition contains a new chapter on multidimensional Brownian motion and its relationship to partial differential equations (PDEs), an advanced topic that is finding new applications. Setting the foundation for this expansion, Chapter 7 now features a proof of Itô's formula. Key exercises that previously were simply proofs left to the reader have been directly inserted into the text as lemmas. The new edition re-instates discussion about the central limit theorem for martingales and stationary sequences.

  • 1 - Bayesian Inference

  • M. Antónia Amaral Turkman, Universidade de Lisboa, Carlos Daniel Paulino, Universidade de Lisboa, Peter Müller, University of Texas, Austin
  • Book:
    Computational Bayesian Statistics
    Published online:
    18 February 2019
    Print publication:
    28 February 2019, pp 1-16
    • Chapter
      • You have access
    • PDF
    • Export citation

Page 33 of 136