Design And Modeling for Computer Experiments

故事就是人物，人物就是故事 ˙以《故事的解剖》暢銷全球25年，好萊塢編劇教父最新力作 ˙英國《衛報》讚譽：「亞理斯多德後最有影響力的故事理論家」 ˙2017年榮獲Final Draft編劇專業獎項Screenwriters Choice Awards之終身成就獎 ◆ 故事自身可能受限於時代意義而褪色， 能夠超越時空永存不朽的，是故事裡的人物。 羅伯特．麥基「故事學三部曲」最終章！ 繼暢銷全球25年的編劇聖經《故事的解剖》（Story, 1997）、對白寫作第一本指南《對白的解剖》（Dialogue, 2016）之後，全球公認的故事專家奉上「故事學三部曲」最終章，教你如何為電影、電視影集和舞台劇、長篇或短篇小說，打造超越時空局限、永存人心的人物╱角色！ ◆ 過去的角色塑造、人物設定教戰手冊為何無法幫到你？ 因為你需要的不只是方法、步驟， 因為你需要先從頭理解「好的故事人物」需要怎樣的作者，練就「頂尖創作者體質」。 ●故事創作者10大能力值：品味、知識、原創性、表演技巧、觀眾╱讀者意識、精通形式、痛恨老套、道德想像力、最好的自我、認識自我 故事人物只要「活靈活現」就夠了嗎？ 不論是從故事發展出人物，抑或從人物發展出故事， 讓人物成功的關鍵，不能靠Step by Step的方法論， 而是需要與你的生命連動的創作思考，從你自身開展出屬於全體人物的宇宙。 ●你筆下的人物不等於你，但會反映你的信念、價值觀、觀察力……從對自己的理解出發，推演出只屬於你的人性觀，讓主角與配角用他們的人生提出所有可能性，構成故事與人物的完整宇宙。 沒有研究，如何開發？！ 創作者該如何從記憶力、想像力出發，從何處學習，在哪裡行動？ 讓「故事學大師」麥基告訴你 什麼是真正的角色研究： 理解「由外而內」vs.「由內到外」的人物形塑方式， 從人物的對立面與衝突面，展現其層次與複雜性。 ‧‧‧ 「我對人性的所有理解，都來自於我自己。」――契訶夫（Anton Chekhov） 麥基：「人物╱角色，終究要在自身裡尋找。」 因為，所有傑出的人物塑造 皆始於自我認識，也終於自我認識。 繼故事、對白的創作解析之後，這一次，麥基要帶領你從根本去認識故事人物╱角色的本質，再深入探究創作好角色的關鍵，比如： ◆人物的發想，是要先塑造外在條件再回頭打造內在核心，還是應該反過來？ ◆如何「進入」你的人物，挖掘他的「四個自我」，以

In Hong Kong, the banking system is the primary source of financial stability risk. Post-2008 regulatory reforms have focused on financial stability policies and tools while neglecting the design of supervisory models. This book provides a comparative analysis of how supervisory models affect the management of financial stability regulations in Hong Kong's banking system. Regulatory issues discussed span prudential regulations, systemically important banks, unconventional liquidity tools, deposit insurance, lender of last resort, resolution regimes, central clearing counterparties and derivatives, Renminbi infrastructure, stock and bond connect schemes, distributed ledger technology, digital yuan, US dollar sanctions, cryptocurrencies, RegTech, and FinTech. A Regulatory Design for Financial Stability in Hong Kong elucidates the flaws and synergies in Hong Kong's banking regulatory framework and proposes conventional and innovative regulatory reforms. This book will be of great interest

This compact course is written for the mathematically literate reader who wants to learn to analyze data in a principled fashion. The language of mathematics enables clear exposition that can go quite deep, quite quickly, and naturally supports an axiomatic and inductive approach to data analysis. Starting with a good grounding in probability, the reader moves to statistical inference via topics of great practical importance – simulation and sampling, as well as experimental design and data collection – that are typically displaced from introductory accounts. The core of the book then covers both standard methods and such advanced topics as multiple testing, meta-analysis, and causal inference.

This compact course is written for the mathematically literate reader who wants to learn to analyze data in a principled fashion. The language of mathematics enables clear exposition that can go quite deep, quite quickly, and naturally supports an axiomatic and inductive approach to data analysis. Starting with a good grounding in probability, the reader moves to statistical inference via topics of great practical importance – simulation and sampling, as well as experimental design and data collection – that are typically displaced from introductory accounts. The core of the book then covers both standard methods and such advanced topics as multiple testing, meta-analysis, and causal inference.