Computing: Naturally random.
نویسنده
چکیده
منابع مشابه
Computing tolerance interval for binomial random variable
Tolerance interval is a random interval that contains a proportion of the population with a determined confidence level and is applied in many application fields such as reliability and quality control. In this educational paper, we investigate different methods for computing tolerance interval for the binomial random variable using the package Tolerance in statistical software R.
متن کاملSIZE AND GEOMETRY OPTIMIZATION OF TRUSS STRUCTURES USING THE COMBINATION OF DNA COMPUTING ALGORITHM AND GENERALIZED CONVEX APPROXIMATION METHOD
In recent years, the optimization of truss structures has been considered due to their several applications and their simple structure and rapid analysis. DNA computing algorithm is a non-gradient-based method derived from numerical modeling of DNA-based computing performance by new computers with DNA memory known as molecular computers. DNA computing algorithm works based on collective intelli...
متن کاملMulti-Coloured Hamilton Cycles In Random Edge-Coloured Graphs
We de ne a space of random edge-coloured graphs Gn;m; which correspond naturally to edge -colourings of Gn;m. We show that there exist constants K0; K1 21 such that provided m K0n logn and K1n then a random edge coloured graph contains a multi-coloured Hamilton cycle with probability tending to 1, as the number of vertices n tends to in nity.
متن کاملINTERVAL ANALYSIS-BASED HYPERBOX GRANULAR COMPUTING CLASSIFICATION ALGORITHMS
Representation of a granule, relation and operation between two granules are mainly researched in granular computing. Hyperbox granular computing classification algorithms (HBGrC) are proposed based on interval analysis. Firstly, a granule is represented as the hyperbox which is the Cartesian product of $N$ intervals for classification in the $N$-dimensional space. Secondly, the relation betwee...
متن کاملLinear and Parallel Learning of Markov Random Fields
We introduce a new embarrassingly parallel parameter learning algorithm for Markov random fields which is efficient for a large class of practical models. Our algorithm parallelizes naturally over cliques and, for graphs of bounded degree, its complexity is linear in the number of cliques. Unlike its competitors, our algorithm is fully parallel and for log-linear models it is also data efficien...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Nature nanotechnology
دوره 10 12 شماره
صفحات -
تاریخ انتشار 2015