Preprint Article Version 1 This version is not peer-reviewed

Bayesian Count Data Modeling for Finding Technological Sustainability

Version 1 : Received: 11 August 2018 / Approved: 13 August 2018 / Online: 13 August 2018 (07:53:13 CEST)

A peer-reviewed article of this Preprint also exists.

Jun, S. Bayesian Count Data Modeling for Finding Technological Sustainability. Sustainability 2018, 10, 3220. Jun, S. Bayesian Count Data Modeling for Finding Technological Sustainability. Sustainability 2018, 10, 3220.

Journal reference: Sustainability 2018, 10, 3220
DOI: 10.3390/su10093220

Abstract

Technology development changes society and society demands new and innovative technology development. We analyze technology to understand society and technology itself. Many researches have been introduced in various fields. Most of them were about patent analysis. This is because detailed and accurate results of research and development are patented. In this paper, we study on new patent analysis method based on count data model and Bayesian regression analysis. Using count data model, we analyze the technological keywords extracted from the collected patent documents. We use the posterior distribution of Bayesian statistics to reflect the experience and knowledge of the relevant technological experts in the analysis model. Moreover, we apply the proposed model to finding sustainable technologies. Finding and developing sustainable technologies is an important activity for companies and research institutes to maintain their technological competitiveness. To illustrate how our modeling could be applied to real domain, we carry out a case study using the patent documents related to artificial intelligence.

Subject Areas

count data; Bayesian regression; technological sustainability; Poisson probability distribution; patent analysis

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