Bitcoin is the first decentralized peer-to-peer payment network that is powered by its users with no central authority or middlemen. The objective of this study is to evaluate the normality of data distribution for exchange rate of Bitcoin. The method implemented in this study is Shapiro-Wilk normality test including graphical approach namely box plot .Results show the data distribution of exchange rate for Bitcoin follows non-normal distribution. Therefore, the normality transformation is important to make sure the distribution of data follows normal distribution. The normal distribution is very crucial as one of the requirement for validity of statistical test.Normality tests are used to determine if a data set is well-modeled by a normal distribution and to compute how likely it is for a random variable underlying the data set to be normally distributed.This study implemented two-stages of outliers detection and deletion process.The final results shows the distribution of Bitcoin exchange rate with first difference is follow normal distribution with probability of 0.722.Result concluded the distribution of data after second stages of outlies deletion treatment shows high normal distribution characteristics. This finding concludes that Bitcoin data is highly volatile with existence of many outliers. The transformation process is highly important to make sure the Bitcoin data follows normal distribution that underlying critical assumption for statistical tests.
Stuart H. Rubin, Thouraya Bouabana‐Tebibel, Yasmine Hoadjli, Kadaouia Habib · 5 authors
The solution of NP-hard problems requires the use of one or more explicit or implicit heuristics as a practical measure. Quantum computers promise to make this practical for O (2n) problems or less, but have yet to deliver a solution to a single NP-hard problem. The question addressed by this paper is whether domain transference and reuse of problem-solving knowledge can be mediated through the reuse of heuristics, and, if so, the extent to which such transference may occur in the solution of NP-hard problems. Neural networks have zero domain transference on account of their inability to represent modus ponens. Similarly, CBR, deep learning, EP, GAs, SVMs, the predicate calculus, learning via conventional expert systems, and all other machine learning technologies are unable to theoretically or practically mediate domain transference because they don't respect randomization as the core underpinning technology. The paper offers a constructive proof of the unbounded density of knowledge in support of the Semantic Randomization Theorem (SRT). It details this result and its potential impact on the machine learning community.