Every blot's an inspiration Every line is free Unlock your imagination Draw what you see Hirameki - 'brainwave' or 'flash of inspiration' in Japanese - is where doodling and imagination come together. Simply put, it's the art of turning a random blot into something amazing, just by adding a few dots and lines. If it's true that you can find happiness in little things, this book should keep your eyes, hand and brain entertained for hours.

36 placemats for mealtime doodling fun!

Draw funny dogs and get into a sunny mood. Draw comical cats and give your day a boost. If you feel like fun and have a pen. It's time to follow your imagination!. Hirameki - 'brainwave' or 'flash of inspiration' in Japanese - is where doodling and imagination come together. Simply put, it's the art of turning a random blot into something amazing, just by adding a few dots and lines. If it's true that you can find happiness in little things, this book should keep your eyes, hand and brain entertained for hours.

Every sky's an inspiration, Every cloud floats free, Unlock your imagination, Draw what you see! Hirameki - 'brainwave' or 'flash of inspiration' in Japanese - is where doodling and imagination come together. Simply put, it's the art of turning a random blot into something amazing, just by adding a few dots and lines. If it's true that you can find happiness in little things, this book should keep your eyes, hand and brain entertained for hours.

Numerical methods in finance have emerged as a vital field at the crossroads of probability theory, finance and numerical analysis. Based on presentations given at the workshop Numerical Methods in Finance held at the INRIA Bordeaux (France) on June 1-2, 2010, this book provides an overview of the major new advances in the numerical treatment of instruments with American exercises. Naturally it covers the most recent research on the mathematical theory and the practical applications of optimal stopping problems as they relate to financial applications. By extension, it also provides an original treatment of Monte Carlo methods for the recursive computation of conditional expectations and solutions of BSDEs and generalized multiple optimal stopping problems and their applications to the valuation of energy derivatives and assets. The articles were carefully written in a pedagogical style and a reasonably self-contained manner. The book is geared toward quantitative analysts, probabilists, and applied mathematicians interested in financial applications.

Numerical methods in finance have emerged as a vital field at the crossroads of probability theory, finance and numerical analysis. Based on presentations given at the workshop Numerical Methods in Finance held at the INRIA Bordeaux (France) on June 1-2, 2010, this book provides an overview of the major new advances in the numerical treatment of instruments with American exercises. Naturally it covers the most recent research on the mathematical theory and the practical applications of optimal stopping problems as they relate to financial applications. By extension, it also provides an original treatment of Monte Carlo methods for the recursive computation of conditional expectations and solutions of BSDEs and generalized multiple optimal stopping problems and their applications to the valuation of energy derivatives and assets. The articles were carefully written in a pedagogical style and a reasonably self-contained manner. The book is geared toward quantitative analysts, probabilists, and applied mathematicians interested in financial applications.

This book presents some new concentration inequalities for Feynman-Kac particle processes. It analyzes different types of stochastic particle models, including particle profile occupation measures, genealogical tree based evolution models, particle free energies, as well as backward Markov chain particle models. It illustrates these results with a series of topics related to computational physics and biology, stochastic optimization, signal processing and Bayesian statistics, and many other probabilistic machine learning algorithms. Special emphasis is given to the stochastic modeling, and to the quantitative performance analysis of a series of advanced Monte Carlo methods; including particle filters, genetic type island models, Markov bridge models, and interacting particle Markov chain Monte Carlo methodologies.