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Titlebook: Advanced Monte Carlo for Radiation Physics, Particle Transport Simulation and Applications; Proceedings of the M Andreas Kling,Fernando J.

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11#
發(fā)表于 2025-3-23 12:58:25 | 只看該作者
Comparison of EGS4 and Measurements Regarding K-X ray and Bremsstrahlung Photonsdes to Python, this book illustrates the key signal and plotting modules that can ease this transition. For those already comfortable with the scientific Python toolchain, this book illustrates the fundamental concepts in signal processing and provides a gateway to further signal processing concepts..?.978-3-319-34357-0978-3-319-01342-8
12#
發(fā)表于 2025-3-23 15:08:26 | 只看該作者
13#
發(fā)表于 2025-3-23 21:25:49 | 只看該作者
Monte Carlo Polarimetric Efficiency Simulations for a Single Monolithic CdTe Thick Matrixp-start?your research, this is the book for you...What You‘ll Learn?.Write Python scripts to automate your lab calculations.Search for important motifs in genome sequences.Use object-oriented programming with Python.Study mining interaction network data for patterns.Review dynamic modeling of bioche
14#
發(fā)表于 2025-3-24 01:47:33 | 只看該作者
Low-Energy Electron Scattering in Solids — a Monte Carlo Approachlready program in Python and wish to learn about marketing applications; and undergraduate or graduate marketing students with little or no programming background. It presumes only an introductory level of familiarity with formal statistics and contains a minimum of mathematics.?.978-3-030-49722-4978-3-030-49720-0
15#
發(fā)表于 2025-3-24 05:59:05 | 只看該作者
ing methods that help generate samples from complicated generic distributions. The chapter ends with a discussion of important probability inequalities that prove to be useful in later statistics and machine learning chapters.
16#
發(fā)表于 2025-3-24 09:14:06 | 只看該作者
17#
發(fā)表于 2025-3-24 12:08:34 | 只看該作者
18#
發(fā)表于 2025-3-24 14:57:05 | 只看該作者
Monte Carlo Simulation of Few-keV Positrons Penetrating in Solidsw section on survival analysis has been included as well as substantial development of Generalized Linear Models. The new deep learning section for image processing includes an in-depth discussion of gradient d978-3-030-18547-3978-3-030-18545-9
19#
發(fā)表于 2025-3-24 22:27:03 | 只看該作者
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發(fā)表于 2025-3-25 00:13:35 | 只看該作者
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