A large number of scientists and engineers employ Monte Carlo simulation and related global optimization techniques (such as simulated annealing) as an essential tool in their work. For such scientists, there is a need to keep up to date with several recent advances in Monte Carlo methodologies such as cluster methods, data- augmentation, simulated tempering and other auxiliary variable methods. There is also a trend in moving towards a population-based approach. All these advances in one way or another were motivated by the need to sample from very complex distribution for which traditional methods would tend to be trapped in local energy minima. It is our aim to provide a self-contained and up to date treatment of the Monte Carlo method to this audience. The Monte Carlo method is a computer-based statistical sampling approach for solving numerical problems concerned with a complex system. The methodology was initially developed in the field of statistical physics during the early days of electronic computing (1945-55) and has now been adopted by researchers in almost all scientific fields. The fundamental idea for constructing Markov chain based Monte Carlo algorithms was introduced in the 1950s. This idea was later extended to handle more and more complex physical systems. In the 1980s, statisticians and computer scientists developed Monter Carlo-based algorithms for a wide variety of integration and optimization tasks. In the 1990s, the method began to play an important role in computational biology. Over the past fifty years, reasearchers in diverse scientific fields have studied the Monte Carlo method and contributed to its development. Today, a large number of scientisits and engineers employ Monte Carlo techniques as an essential tool in their work. For such scientists, there is a need to keep up-to-date with recent advances in Monte Carlo methodologies.
發表於2024-11-07
Monte Carlo Strategies in Scientific Computing 2024 pdf epub mobi 電子書 下載
第一個公式說g(x)在n維空間D上的積分I,可以通過從D空間隨機抽取m個點x(1) x(2) ... x(m)計算Im=1/m*( g(x(1))+g(x(2))+...+g(x(m)) ),當m->無窮時,lim(Im)=I.為什麼我始終感覺這個還要乘上空間D的n維體積(或者說D的測度L(D)?)呢?這個看不明白,後麵的東西就看得稀裏糊塗的。
評分第一個公式說g(x)在n維空間D上的積分I,可以通過從D空間隨機抽取m個點x(1) x(2) ... x(m)計算Im=1/m*( g(x(1))+g(x(2))+...+g(x(m)) ),當m->無窮時,lim(Im)=I.為什麼我始終感覺這個還要乘上空間D的n維體積(或者說D的測度L(D)?)呢?這個看不明白,後麵的東西就看得稀裏糊塗的。
評分第一個公式說g(x)在n維空間D上的積分I,可以通過從D空間隨機抽取m個點x(1) x(2) ... x(m)計算Im=1/m*( g(x(1))+g(x(2))+...+g(x(m)) ),當m->無窮時,lim(Im)=I.為什麼我始終感覺這個還要乘上空間D的n維體積(或者說D的測度L(D)?)呢?這個看不明白,後麵的東西就看得稀裏糊塗的。
評分第一個公式說g(x)在n維空間D上的積分I,可以通過從D空間隨機抽取m個點x(1) x(2) ... x(m)計算Im=1/m*( g(x(1))+g(x(2))+...+g(x(m)) ),當m->無窮時,lim(Im)=I.為什麼我始終感覺這個還要乘上空間D的n維體積(或者說D的測度L(D)?)呢?這個看不明白,後麵的東西就看得稀裏糊塗的。
評分第一個公式說g(x)在n維空間D上的積分I,可以通過從D空間隨機抽取m個點x(1) x(2) ... x(m)計算Im=1/m*( g(x(1))+g(x(2))+...+g(x(m)) ),當m->無窮時,lim(Im)=I.為什麼我始終感覺這個還要乘上空間D的n維體積(或者說D的測度L(D)?)呢?這個看不明白,後麵的東西就看得稀裏糊塗的。
圖書標籤: 濛特卡洛 模擬計算 統計 隨機 MCMC 統計學 數學 Statistics
這本是書bible! 劉老師真的是世界上最懂monte Carlo直覺最好的人瞭。希望能少點typo,不要每次it is easy to see我都要推公式半個小時纔發現他寫錯瞭。
評分很早之前就放棄瞭!中文都搞不明白,英文看到第二章就放棄瞭!
評分很早之前就放棄瞭!中文都搞不明白,英文看到第二章就放棄瞭!
評分這本是書bible! 劉老師真的是世界上最懂monte Carlo直覺最好的人瞭。希望能少點typo,不要每次it is easy to see我都要推公式半個小時纔發現他寫錯瞭。
評分很早之前就放棄瞭!中文都搞不明白,英文看到第二章就放棄瞭!
Monte Carlo Strategies in Scientific Computing 2024 pdf epub mobi 電子書 下載