Hasil Pencarian  ::  Simpan CSV :: Kembali

Hasil Pencarian

Ditemukan 167 dokumen yang sesuai dengan query
cover
Roussas, George G.
Abstrak :
Introduction to probability, is written for upper-level undergraduate students in statistics, mathematics, engineering, computer science, operations research, actuarial science, biological sciences, economics, physics, and some of the social sciences. With his trademark clarity and economy of language, the author explains important concepts of probability, while providing useful exercises and examples of real world applications for students to consider. After introducing fundamental probability concepts, the book proceeds to topics including special distributions, the joint probability density function, covariance and correlation coefficients of two random variables, and more.
Oxford, UK: Academic Press, 2014
e20426998
eBooks  Universitas Indonesia Library
cover
Breiman, Leo
Abstrak :
Well known for the clear, inductive nature of its exposition, this reprint volume is an excellent introduction to mathematical probability theory. It may be used as a graduate-level text in one- or two-semester courses in probability for students who are familiar with basic measure theory, or as a supplement in courses in stochastic processes or mathematical statistics. Designed around the needs of the student, this book achieves readability and clarity by giving the most important results in each area while not dwelling on any one subject. Each new idea or concept is introduced from an intuitive, common-sense point of view. Students are helped to understand why things work, instead of being given a dry theorem-proof regime.
Philadelphia: Society for Industrial and Applied Mathematics, 1992
e20448050
eBooks  Universitas Indonesia Library
cover
Donati-Martin, Catherine, editor
Abstrak :
As usual, some of the contributions to this 44th Séminaire de Probabilités were presented during the Journées de Probabilités held in Dijon in June 2010. The remainder were spontaneous submissions or were solicited by the editors. The traditional and historical themes of the Séminaire are covered, such as stochastic calculus, local times and excursions, and martingales. Some subjects already touched on in the previous volumes are still here: free probability, rough paths, limit theorems for general processes (here fractional Brownian motion and polymers), and large deviations. Lastly, this volume explores new topics, including variable length Markov chains and peacocks. We hope that the whole volume is a good sample of the main streams of current research on probability and stochastic processes, in particular those active in France.
Berlin : [Springer , ], 2012
e20419392
eBooks  Universitas Indonesia Library
cover
Ratu Mutiara Pakungwati
Abstrak :
Tugas akhir ini berisi pembahasan mengenai distribusi Invers Weibull Marshall-Olkin IWMO yang merupakan distribusi probabilitas untuk peubah acak kontinu. Distribusi IWMO dibentuk dari distribusi Invers Weibull IW dengan metode Marshall-Olkin, metode ini adalah metode penambahan parameter yang diperkenalkan oleh Albert W Marshall dan Ingram Olkin pada tahun 1997. Distribusi IW sendiri diperoleh dari distribusi Weibull dengan melakukan tranformasi terhadap peubah acak. Distribusi IWMO mampu menggambarkan bentuk data seperti distribusi asalnya dalam hal ini distribusi IW dan bentuk data dari distribusi invers Eksponensial selain itu distribusi IWMO dapat menjelaskan data outlier lebih baik dibandingkan distribusi IW disebabkan oleh penambahan parameter Marshall-Olkin. Selanjutnya akan dibahas mengenai fungsi kepadatan probabilitas, fungsi distribusi, Moment Generating Function MGF, momen ke-r, mean, variansi, koefisien skewness, koefisien kutrosis, kuantil dan median dari IWMO. Penaksiran parameter menggunakan metode maksimum likelihood. Distribusi Weibull, IW dan IWMO akan diterapkan pada data yang memiliki outlier. Perbandingan model menggunakan log likelihood, AIC, BIC menunjukan distribusi IWMO sesuai dengan data lebih baik dibandingkan Weibull dan IW. ......This final project contains a discussion of the distribution of Inverse Weibull Marshall Olkin IWMO which is the probability distribution for continuous random variables. The IWMO distribution is formed from the Inverse Weibull IW distribution by Marshall Olkin method, this method is the parameter addition method introduced by Albert W Marshall and Ingram Olkin in 1997. IWull distribution itself is obtained from the Weibull distribution by transforming the random variables. IWMO distribution able to describe data form like its original distribution that is IW distribution and data form from Exponential inverse distribution beside that IWMO distribution can explain data outlier better than IW distribution caused by addition of Marshall Olkin parameter. The next will be discussed about probability density function, distribution function, Moment Generating Function MGF, rth moment, mean, variance, skewness coefficient, coefficient kutrosis, quantitative and median from IWMO. Parameter estimation using likelihood maximum method. Weibull, IW and IWMO distributions will be applied to data that has an outlier. Comparison of models using log likelihood, AIC, BIC shows IWMO distribution in accordance with better data than Weibull and IW.
Depok: Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Indonesia, 2018
S-Pdf
UI - Skripsi Membership  Universitas Indonesia Library
cover
Fitria Rahmawati
Abstrak :
Data lifetime biasanya digunakan peneliti untuk mengetahui tingkat survival atau tingkat kegagalan suatu objek. Distribusi Weibull merupakan distribusi probabilitas yang sering digunakan untuk memodelkan data lifetime. Namun, distribusi Weibull hanya dapat memodelkan data lifetime dengan tingkat kegagalan atau hazard rate yang monoton. Sehingga dibutuhkan distribusi baru yang dapat memodelkan data lifetime dengan karakteristik tingkat kegagalan atau hazard rate yang beragam. Distribusi inverse Weibull adalah distribusi hasil transformasi inverse dari distribusi Weibull. Distribusi inverse Weibull merupakan distribusi yang dapat memodelkan data lifetime dengan hazard rate monoton (turun) maupun  non-monoton (upside-down bathtub shaped). Namun, untuk membuat kepadatan fleksibel dengan berbagai macam bentuk diperlukan generalisasi dari distribusi ini dengan menambahkan suatu parameter shape. Distribusi generalized inverse Weibull merupakan generalisasi dari distribusi inverse Weibull yaitu yang dibentuk dengan memangkatkan fungsi distribusi inverse Weibull dengan suatu parameter baru. Distribusi generalized inverse Weibull memiliki 2 parameter shape dan 1 parameter scale sehingga distribusi ini dapat menggambarkan shape dari fungsi hazard yang lebih beragam. Pada  skripsi ini, akan dibahas mengenai pembentukan distribusi inverse Weibull dan pembentukan distribusi generalized inverse Weibull, serta fungsi kepadatan probabilitas, fungsi distribusi, fungsi survival, fungsi hazard, dan karakteristik-karakteristik dari kedua distribusi tersebut. Penaksiran parameter dari distribusi generalized inverse Weibull menggunakan metode maksimum likelihood.
Lifetime data is usually used by researchers to determine the level of survival or failure rate of an object. Weibull distribution is a probability distribution that is often used to model the lifetime data. However, the Weibull distribution is only used to model the lifetime data with monotone failure rate or monotone hazard rate. So that, a new distribution is needed to model the lifetime data with varying characteristics of failure rates or hazard rates. Inverse Weibull distribution is a distribution that is formed from the inverse transformation of the Weibull distribution. Inverse Weibull distribution is a continued distribution which can model lifetime data with a monotone hazard rate (constant, increase, and decrease) or non-monotone hazard rate (upside-down bathtub shaped). However, to make a density flexible with wide variety of shapes the generalizations from this distribution are needed by adding a shape parameter. Generalized inverse Weibull distribution is derived from generalization of inverse Weibull distribution that is formed by raising the inverse Weibull distribution function with a new parameter. Generalized inverse Weibull distribution has two shape parameters and one scale parameter. So, this distribution can describe a more diverse shapes of hazard function. In this skripsi, we will discuss how to construct inverse Weibull distribution and Generalized inverse Weibull distribution, and probability distribution function, cumulative distribution function, survival function, hazard function, and characteristics of these distributions. Parameter estimation of the generalized inverse Weibull distribution is using the maximum likelihood method.
Depok: Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Indonesia, 2019
S-pdf
UI - Skripsi Membership  Universitas Indonesia Library
cover
Scheaffer, Richard L.
California: Duxbury Press, 1995
519.2 SCH p
Buku Teks  Universitas Indonesia Library
cover
Montgomery, Douglas C.
New York: John Wiley & Sons, 2004
519.5 MON a
Buku Teks  Universitas Indonesia Library
cover
Blum, Julius R.
Philadelphia: W.B. Saunders, 1972
519.2 BLU p
Buku Teks  Universitas Indonesia Library
cover
Meyer, Paul L.
Reading, Massachusetts: Addison-Wesley, 1970
519.1 MEY i
Buku Teks  Universitas Indonesia Library
cover
Mosteller, Frederick
Bandung: ITB Press, 1988
519.2 MOS p
Buku Teks  Universitas Indonesia Library
<<   1 2 3 4 5 6 7 8 9 10   >>