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"A study of those statistical ideas that use a probability distribution over parameter space. The first part describes the axiomatic basis in the concept of coherence and the implications of this for sampling theory statistics. The second part discusses the use of Bayesian ideas in many branches of statistics."

"Prescriptive Bayesian decision making has reached a high level of maturity and is well-supported algorithmically. However, experimental data shows that real decision makers choose such Bayes-optimal decisions surprisingly infrequently, often making decisions that are badly sub-optimal. So prevalent is such imperfect decision-making that it should be accepted as an inherent feature of real decision makers living within interacting societies.To date such societies have been investigated from an economic and gametheoretic perspective, and even to a degree from a physics perspective. However, little research has been done from the perspective of computer science and associated disciplines like machine learning, information theory and neuroscience. This book is a major contribution to such research."

"This book, deals with the aspects of modeling and solving real-world optimization problems in a unique combination. It treats systematically the major algebraic modeling languages (AMLs) and modeling systems (AMLs) used to solve mathematical optimization problems. AMLs helped significantly to increase the usage of mathematical optimization in industry."

"Bayesian Nonparametrics via Neural Networks is the first book to focus on neural networks in the context of nonparametric regression and classification, working within the Bayesian paradigm. Its goal is to demystify neural networks, putting them firmly in a statistical context rather than treating them as a black box. This approach is in contrast to existing books, which tend to treat neural networks as a machine learning algorithm instead of a statistical model. Once this underlying statistical model is recognized, other standard statistical techniques can be applied to improve the model.The Bayesian approach allows better accounting for uncertainty. This book covers uncertainty in model choice and methods to deal with this issue, exploring a number of ideas from statistics and machine learning. A detailed discussion on the choice of prior and new noninformative priors is included, along with a substantial literature review. Written for statisticians using statistical terminology, Bayesian Nonparametrics via Neural Networks will lead statisticians to an increased understanding of the neural network model and its applicability to real-world problems.To illustrate the major mathematical concepts, the author uses two examples throughout the book: one on ozone pollution and the other on credit applications. The methodology demonstrated is relevant for regression and classification-type problems and is of interest because of the widespread potential applications of the methodologies described in the book."

"The handbook of networks in power systems includes the state-of-the-art developments that occurred in the power systems networks, in particular gas, electricity, liquid fuels, freight networks, as well as their interactions. The book is separated into two volumes with three sections, where one scientific paper or more are included to cover most important areas of networks in power systems. The first volume covers topics arising in electricity network, in particular electricity markets, smart grid, network expansion, as well as risk management. The second volume presents problems arising in gas networks, such as scheduling and planning of natural gas systems, pricing, as well as optimal location of gas supply units. In addition, the second volume covers the topics of interactions between energy networks. Each subject is identified following the activity on the domain and the recognition of each subject as an area of research. The scientific papers are authored by world specialists on the domain and present either state-of-the-arts reviews or scientific developments."

"ABSTRAKPenelitian ini bertujuan untuk melihat faktor-faktor yang mempengaruhi tingkat penerimaan teknologi Aplikasi Laporan Pertanggungjawaban Keuangan Bantuan Operasional Sekolah (ALPEKA BOS). Kemudahan dalam penggunaan dipengaruhi oleh enam faktor, yaitu kepercayaan diri dalam menggunakan komputer, persepsi mengenai teknologi, kecemasan dalam menggunakan komputer, keterbukaan terhadap komputer, rasa senang, dan penggunaan objektif. Faktor utama dalam Technology Acceptance Model yang digunakan dalam penelitian ini adalah kemudahan dalam menggunakan, persepsi manfaat dan minat untuk menggunakan aplikasi.Survei dilakukan pada 260 Sekolah Dasar Negeri di Kabupaten Pandeglang, Banten, dengan teknik purposive sampling. Analisis dilakukan menggunakan Structural Equation Modeling. Hasil penelitian menunjukkan bahwa faktor yang jadi penentu kemudahan menggunakan aplikasi adalah kepercayaan diri, persepsi mengenai teknologi, kecemasan, dan rasa senang. Persepsi kemudahan dalam menggunakan aplikasi merupakan faktor yang mempengaruhi persepsi manfaat dan juga minat menggunakan aplikasi.

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ABSTRACTThis study aims to analyze the factors which could affect user?s acceptancte technology of Aplikasi Laporan Pertanggungjawaban Keuangan Bantuan Operasional Sekolah (Application for Financial Accountability Reports School Operation Assistance). Perceived ease of use affected by six factors, they are computer self efficacy, perceptions of external control, computer anxiety, computer playfulness, perceived enjoyment, and objective usability. The main factor of the technology acceptance model are perceived ease of use, perceived usefulness and behavioral intention to use.Survey conducted on 260 user in public elementary school in Pandeglang district, Banten, using purposive sampling method. Analysis was performed using Structural Equation Modeling. The result showed determinants factor perceived ease of use are computer self efficacy, perceived external control, computer anxiety, and perceived enjoyment. Perceived ease of use is a factor which affect perceived usefulness and behavioral intention to use."

"ABSTRAKData bawah permukaan merupakan data yang sangat dibutuhkan dalam menentukan besar cadangan hidrokarbon di Indonesia. Selain itu, kualitas dari cadangan itu sendiri menentukan perkembangan industri migas kedepannya. Pemerintah telah secara agresif mendorong penggunaan gas alam dan saat ini pemerintah belum memiliki data mengenai cadangan gas secara efisien. Oleh karena itu, diperlukan penyusunan dalam manajemen data reservoir, khususnya reservoir gas. Penelitian ini menyajikan konsep sehingga pemerintah dapat dengan mudah melihat kualitas cadangan gas yang berasal dari data bawah permukaan. Dalam penelitian ini, manajemen data dilakukan dengan cara mengelompokkan data mentah sesuai parameter dari sistem evaluasi Sumber Daya Cadangan (eSDC). Salah satu sistem pengolahan data untuk analisa kualitas berproduksi suatu reservoir gas, menggunakan Bayesian Hierarchical Softmax Regression dengan perhitungan Markov Chain Monte Carlo untuk penyelesaian integral multi dimensi dari Bayesian. Penelitian ini menunjukkan bahwa dengan metode yang digunakan, dapat memprediksi keyakinan kualitas reservoir untuk berproduksi dan memberikan informasi ketidakpastian atas prediksi tersebut. Pada eSDC, terdapat lima klasifikasi status lapangan di Indonesia yaitu, on production, production on hold, production justified, production pending, dan recently discovered. Pada klasifikasi status On Production dengan 100 data lapangan gas, menghasilkan nilai precision 81%, recall 98%, dan f-measured sebesar 89%. Dengan demikian, dapat dikatakan bahwa lapangan gas dengan klasifikasi on production, keyakinan reservoir dalam berproduksi secara komersil tinggi.

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ABSTRACTSubsurface data is data that is needed to determine the amount of hydrocarbon reserves in Indonesia. In addition, the quality of the reserves itself determines the future development of the oil and gas industry. The government has aggressively encouraged the use of natural gas and currently the government does not have data on gas reserves efficiently. Therefore, it is necessary to arrange in the management of reservoir data, especially gas reservoirs. This research presents a concept so that the government can easily see the quality of gas reserves from subsurface data. In this study, data management is done by grouping raw data according to parameters of the Reserve Resources evaluation system (eSDC). One of the data processing systems for analyzing the quality of producing a gas reservoir, using Bayesian Hierarchical Softmax Regression with Markov Chain Monte Carlo calculations for solving multi-dimensional integrals from Bayesian. This study shows that with the method used, it can predict reservoir quality beliefs for production and provide uncertainty information on these predictions. In eSDC, there are five classifications of field status in Indonesia, namely, on production, production on hold, production justified, production pending, and recently discovered. In the On Production status classification with 100 gas field data, it produces a precision value of 81%, recall 98%, and f-measured of 89%. Thus, it can be said that the gas field with the classification of on production, reservoir confidence in commercial production is high."

"The book focuses on the next fields of computer science: combinatorial optimization, scheduling theory, decision theory, and computer-aided production management systems. It also offers a quick introduction into the theory of PSC-algorithms, which are a new class of efficient methods for intractable problems of combinatorial optimization. A PSC-algorithm is an algorithm which includes: sufficient conditions of a feasible solution optimality for which their checking can be implemented only at the stage of a feasible solution construction, and this construction is carried out by a polynomial algorithm (the first polynomial component of the PSC-algorithm); an approximation algorithm with polynomial complexity (the second polynomial component of the PSC-algorithm); also, for NP-hard combinatorial optimization problems, an exact subalgorithm if sufficient conditions were found, fulfilment of which during the algorithm execution turns it into a polynomial complexity algorithm. Practitioners and software developers will find the book useful for implementing advanced methods of production organization in the fields of planning (including operative planning) and decision making. Scientists, graduate and master students, or system engineers who are interested in problems of combinatorial optimization, decision making with poorly formalized overall goals, or a multiple regression construction will benefit from this book."

"This book discusses the recent developments in robust optimization (RO) and information gap design theory (IGDT) methods and their application for the optimal planning and operation of electric energy systems. Chapters cover both theoretical background and applications to address common uncertainty factors such as load variation, power market price, and power generation of renewable energy sources. Case studies with real-world applications are included to help undergraduate and graduate students, researchers and engineers solve robust power and energy optimization problems and provide effective and promising solutions for the robust planning and operation of electric energy systems."