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  - [Petr PHILONENKO](https://orcid.org/0000-0002-6295-4470), Ph.D. in Computer Science;
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  - [Sergey POSTOVALOV](https://orcid.org/0000-0003-3718-1936), D.Sc. in Computer Science.
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  **Contents**
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  - [About](#about)
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  - [Citing](#citing)
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  - [main.cpp](#maincpp)
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  - [simulation_for_machine_learning.h](#simulation_for_machine_learningh)
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- # About
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- This dataset is a supplement to the [github repositiry](https://github.com/pfilonenko/ML_for_TwoSampleTesting) and paper addressed to solve the two-sample problem under right-censored observations using Machine Learning.
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- The problem statement can be formualted as H0: S1(t)=S2(t) versus H: S1(t)≠S_2(t) where S1(t) and S2(t) are survival functions of samples X1 and X2.
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-
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- This dataset contains the synthetic data simulated by the Monte Carlo method and Inverse Transform Sampling.
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-
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  # Citing
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  ~~~
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  @misc {petr_philonenko_2024,
 
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  - [Petr PHILONENKO](https://orcid.org/0000-0002-6295-4470), Ph.D. in Computer Science;
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  - [Sergey POSTOVALOV](https://orcid.org/0000-0003-3718-1936), D.Sc. in Computer Science.
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+ # About
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+ This dataset is a supplement to the [github repositiry](https://github.com/pfilonenko/ML_for_TwoSampleTesting) and paper addressed to solve the two-sample problem under right-censored observations using Machine Learning.
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+ The problem statement can be formualted as H0: S1(t)=S2(t) versus H: S1(t)≠S_2(t) where S1(t) and S2(t) are survival functions of samples X1 and X2.
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+
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+ This dataset contains the synthetic data simulated by the Monte Carlo method and Inverse Transform Sampling.
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+
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  **Contents**
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  - [About](#about)
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  - [Citing](#citing)
 
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  - [main.cpp](#maincpp)
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  - [simulation_for_machine_learning.h](#simulation_for_machine_learningh)
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  # Citing
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  ~~~
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  @misc {petr_philonenko_2024,