Militia
发表于 2025-3-23 12:18:12
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骗子
发表于 2025-3-23 14:05:28
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杂役
发表于 2025-3-23 20:36:37
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嬉耍
发表于 2025-3-23 23:14:45
Characteristic Functions and the Central Limit Theorem,The main goal of this chapter is the central limit theorem (CLT) for sums of independent random variables (Theorem 15.37) and for independent arrays of random variables (Lindeberg-Feller theorem, Theorem 15.43). For the latter, we prove only that one of the two implications (Lindeberg’s theorem) that is of interest in the applications.
institute
发表于 2025-3-24 03:13:07
Convergence of Markov Chains,We consider a Markov chain . with invariant distribution π and investigate conditions under which the distribution of . converges to π for . → ∞. Essentially it is necessary and sufficient that the state space of the chain cannot be decomposed into subspaces
FUSC
发表于 2025-3-24 10:01:28
Achim KlenkeComprehensive and modern introduction to the most important fields of probability theory.Unique selection of topics, including many not usually found in introductory texts.Includes supplementary mater
Cholesterol
发表于 2025-3-24 13:13:35
0172-5939 dition, plenty of figures, computer simulations, biographic details of key mathematicians, and a wealth of examples support and enliven the presentation. .978-1-84800-048-3Series ISSN 0172-5939 Series E-ISSN 2191-6675
BARGE
发表于 2025-3-24 14:54:14
Textbook 20081st editioner to display the power of the abstract concepts in the world of probability theory. In addition, plenty of figures, computer simulations, biographic details of key mathematicians, and a wealth of examples support and enliven the presentation. .
MANIA
发表于 2025-3-24 21:09:39
pairwise interactions, and iii) A Content-Based Embedding model, which overcomes the cold start issue. The empirical study on real-world datasets proves that E-MIGAN achieves state-of-the-art performance, demonstrating its effectiveness in capturing complex interactions in graph-structured data.
HALL
发表于 2025-3-24 23:34:30
es an imbalance classification strategy with heterophily-aware GNNs to effectively address the class imbalance problem while significantly reducing training time. Our experiments on real-world graphs demonstrate our model’s superiority in classification performance and efficiency for node classifica