Titlebook: Applied Probability; From Random Sequence Valérie Girardin,Nikolaos Limnios Textbook 2018 Springer Nature Switzerland AG 2018 Probability.R

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書目名稱Applied Probability影響因子(影響力)




書目名稱Applied Probability影響因子(影響力)學(xué)科排名




書目名稱Applied Probability網(wǎng)絡(luò)公開度




書目名稱Applied Probability網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Applied Probability被引頻次




書目名稱Applied Probability被引頻次學(xué)科排名




書目名稱Applied Probability年度引用




書目名稱Applied Probability年度引用學(xué)科排名




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書目名稱Applied Probability讀者反饋學(xué)科排名

炸壞

Conditioning and Martingales,st supposed to exist, which is true for all random variables taking values in .. Still, conditional expectation is defined in the most general case. A section is dedicated to determining practically conditional distributions and expectations, and another to the linear model in which a random phenome

繼而發(fā)生

Markov Chains,chains generalize sequences of independent random variables to variables linked by a simple dependence relation. They model for example, phase transitions of substances between solid, liquid and gaseous states, passages of systems between up and down states, etc. A random sequence . is a Markov chai

壁畫

Markov and Semi-Markov Processes,uously counted over the positive real line. Markov processes are continuous-time processes that share the Markov property with the discrete-time Markov chains. Their future evolution conditional to the past depends only on the last occupied state. Their extension to the so-called semi-Markov process

Ordeal

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Continuous Time Stochastic Processes,A stochastic process represents a system, usually evolving along time, which incorporates an element of randomness, as opposed to a deterministic process.

寬度

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EXALT

https://doi.org/10.1007/978-3-319-97412-5Probability; Random sequences; Random processes; Markov chains; Semi-Markov processes; Martingales; Stocha