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"ABSTRAKPrediksi durasi banyak dilakukan oleh para pemangku kepentingan berdasarkan pengalaman atau intuisi mereka. Hal ini menyebabkan perkiraan durasi memiliki risiko kesalahan yang dapat menggangu proses pelaksanaan konstruksi. Penelitian ini mencoba mengembangkan suatu model pendekatan prediksi durasi proyek berdasarkan metode prediksi durasi akhir proyek Earned Schedule, yang merupakan pengembangan metode Earned Value, dan memanfaatkan faktor-faktor pengaruh eksternal yang tersedia dalam informasi resmi. Selain itu penelitian ini mengakomodasi kondisi ketidak pastian yang umum terjadi di lingkungan negara berkembang semisal Indonesia. Ketiga hal tersebut dirangkum dalam suatu model pengembangan prediksi durasi probabilistik berdasarkan faktor pengaruh eksternal. Hasil penelitian menunjukan adanya hubungan antara beberapa faktor pengaruh terukur dengan durasi probabilistik proyek konstruksi. 

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ABSTRACT

The prediction of project duration is mostly calculated based on the experience or intuition of the estimator. This causes the estimated duration to have an error risk that could disrupt the construction process. This research tries to develop a project duration prediction approach model based on the Earned Schedule project's final prediction method, which is the development of the Earned Value Method, and utilizes the external influencing factors available in official information. In addition, this study accommodates uncertainty conditions that are common in developing countries such as Indonesia. These three matters are summarized in a probabilistic duration prediction development model based on external influencing factors. The result of the research shows that there is a sufficient correlation between several factors of measured influence with the probabilistic duration of the construction project. 

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"This volume collects twenty articles on various topics in this field, including self-interacting random walks and polymer models in random and non-random environments, branching processes, Parisi formulas and metastability in spin glasses, and hydrodynamic limits for gradient Gibbs models. The majority of these articles contain original results at the forefront of contemporary research, some of them include review aspects and summarize the state-of-the-art on topical issues, one focal point is the parabolic Anderson model, which is considered with various novel aspects including moving catalysts, acceleration and deceleration and fron propagation, for both time-dependent and time-independent potentials. "

"uilding upon the previous editions, this textbook is a first course in stochastic processes taken by undergraduate and graduate students (MS and PhD students from math, statistics, economics, computer science, engineering, and finance departments) who have had a course in probability theory. It covers Markov chains in discrete and continuous time, Poisson processes, renewal processes, martingales, and option pricing. One can only learn a subject by seeing it in action, so there are a large number of examples and more than 300 carefully chosen exercises to deepen the reader&​#x;s understanding. Drawing from teaching experience and student feedback, there are many new examples and problems with solutions that use TI-83 to eliminate the tedious details of solving linear equations by hand, and the collection of exercises is much improved, with many more biological examples. Originally included in previous editions, material too advanced for this first course in stochastic processes has been eliminated while treatment of other topics useful for applications has been expanded. In addition, the ordering of topics has been improved; for example, the difficult subject of martingales is delayed until its usefulness can be applied in the treatment of mathematical finance. &​#x; A concise treatment and textbook on the most important topics in Stochastic Processes &​#x; Illustrates all concepts with examples and presents more than 300 carefully chosen exercises for effective learning &​#x; New edition includes added and revised exercises, including many biological exercises, in addition to restructured and rewritten sections with a goal toward clarity and simplicity &​#x; Solutions Manual available for instructors Richard Durrett received his Ph.D. in Operations Research from Stanford in 1976. He taught at the UCLA mathematics department for 9 years and at Cornell for 25 years before moving to Duke in 2010. He is author of 8 books and more than 200 journal articles and has supervised more that 45 Ph.D. students. He is a member of the National Academy of Science. Most of his current research concerns the applications of probability to biology: ecology, genetics, and cancer modeling"