000			03536nam a2200277 i 4500
003			CSPC
005			20260820152752.0
008			260820s2024 onc b 001 0 eng d
020			_a9781774698617
040			_aCSPC _beng _cCPSC _erda
050	0	0	_aQA273 _b.A67 2024
082	0	0	_223 _a519.2 _bAp581
245	0	4	_aThe application of probability theory / _cedited by Olga Moreira.
264		1	_aBurlington, Ontario : _bArcler Press, _c2024.
300			_axxiv, 375 pages ; _c24 cm.
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
504			_aIncludes bibliographical references and index.
505	0		_aIntroduction -- Missing data approaches for probability regression models with missing outcomes with applications -- Maximum likelihood estimation for three-parameter weibull distribution using evolutionary strategy -- Probability distribution and deviation information fusion driven support vector regression model and its application -- Cascade source inference in networks: a markov chain Monte Carlo approach -- PICF-LDA: a topic enhanced LDA with probability incremental correction factor for web API service clustering -- The development of a stochastic mathematical model of Alzheimer's disease to help improve the design of clinical trials of potential treatments -- Comparison of neural network and logistic regression analysis to predict the probability of urinary tract infection caused by cystoscopy -- Statistical analysis of orthographic and phonemic language corpus for word-based and phoneme-based polish language modelling -- Detection of fake news and hate speech for Ethiopian languages: a systematic review of the approaches -- Comparison between the Hamiltonian Monte Carlo method and the Mteropolis-Hastings method for coseismic fault model estimation -- Sequential Monte Carlo method toward online RUL assessment with applications -- Probabilistic forecasting of traffic using multikernel based extreme learning machine -- Value-at-risk ambiguity aversion -- DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market prediction.
520			_a"Probability theory is an essential branch of mathematics that provides a robust framework for comprehending and quantifying uncertainty and randomness across diverse domains. Its significance spans a wide range of fields, from statistics and data analysis to machine learning and artificial intelligence, medical and health sciences, natural language processing and information retrieval, and engineering and reliability analysis. In the pages of this book, we embark on a comprehensive exploration of the practical applications of probability theory in these various disciplines, delving into the intricacies of both frequentist and Bayesian perspectives. Our journey commences by thoroughly examining the core principles and concepts that underpin probability theory. We meticulously unravel the intricacies of sample space, events, probability distribution, random variables, probability laws and rules, and expected value. By establishing a solid understanding of these foundational elements, readers will be equipped to comprehend the broader applications of probability theory across different fields"-- Back cover.
650		0	_aProbabilities.
700	1		_aMoreira, Olga, _eeditor.
942			_2ddc _n0 _cBK _e23 _h519.2 _iAp581 _kCIR _m2024
999			_c33027 _d33027