Optimality of Poisson Processes Intensity Learning with Gaussian Processes
Authors: Alisa Kirichenko, Harry van Zanten
JMLR 2015 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we provide theoretical support for the so-called Sigmoidal Gaussian Cox Process approach to learning the intensity of an inhomogeneous Poisson process on a ddimensional domain. This method was proposed by Adams, Murray and Mac Kay (ICML, 2009), who developed a tractable computational approach and showed in simulation and real data experiments that it can work quite satisfactorily. The results presented in the present paper provide theoretical underpinning of the method. |
| Researcher Affiliation | Academia | Alisa Kirichenko EMAIL Harry van Zanten EMAIL Korteweg-de Vries Institute for Mathematics University of Amsterdam P.O. Box 94248, 1090 GE Amsterdam, The Netherlands |
| Pseudocode | No | The paper describes mathematical models, theorems, and proofs. There are no structured pseudocode or algorithm blocks present. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or providing links to a code repository. This is a theoretical paper providing mathematical proofs. |
| Open Datasets | No | The paper does not use or refer to any specific publicly available datasets for experimental evaluation. It describes a theoretical observation model involving 'n independent copies of an inhomogeneous Poisson process' but this refers to the theoretical data generation process, not a real dataset. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments on specific datasets. Therefore, there is no mention of dataset splits for training, validation, or testing. |
| Hardware Specification | No | This is a theoretical paper focused on mathematical proofs and analysis. It does not describe any experiments that would require specific hardware, and thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical concepts and proofs. It does not mention any software or libraries with specific version numbers that would be necessary to replicate experimental results. |
| Experiment Setup | No | This paper provides theoretical analysis and proofs rather than empirical experimental results. Therefore, it does not describe any experimental setup details such as hyperparameters, training configurations, or system-level settings. |