• Search Research Projects
  • Search Researchers
  • How to Use
  1. Back to previous page

Nakakita Shogo  仲北 祥悟

ORCIDConnect your ORCID iD *help
Researcher Number 80855114
Other IDs
Affiliation (Current) 2025: 東京大学, 大学院総合文化研究科, 特任講師
Affiliation (based on the past Project Information) *help 2024 – 2025: 東京大学, 大学院総合文化研究科, 特任助教
2021 – 2022: 東京大学, 大学院総合文化研究科, 特任助教
Review Section/Research Field
Principal Investigator
Basic Section 60030:Statistical science-related / 0201:Algebra, geometry, analysis, applied mathematics,and related fields
Except Principal Investigator
Sections That Are Subject to Joint Review: Basic Section60030:Statistical science-related , Basic Section61030:Intelligent informatics-related / Basic Section 61030:Intelligent informatics-related / Basic Section 60030:Statistical science-related
Keywords
Principal Investigator
計算機統計学 / 確率微分方程式 / エルゴード性 / 確率的最適化 / 確率過程の統計学 / オンライン最適化
Except Principal Investigator
統計的推定・推論 / 深層モデル / 非スパースモデル / 高次元統計学 / 大規模モデル
  • Research Projects

    (3 results)
  • Research Products

    (3 results)
  • Co-Researchers

    (3 People)
  •  データ駆動型集団ダイナミクス解析のための平均場近似モデルの統計的推測手法の開発Principal Investigator

    • Principal Investigator
      仲北 祥悟
    • Project Period (FY)
      2025 – 2027
    • Research Category
      Grant-in-Aid for Early-Career Scientists
    • Review Section
      Basic Section 60030:Statistical science-related
    • Research Institution
      The University of Tokyo
  •  Theoretical development of non-sparse high-dimensional statistics for statistical understanding and utilization of large-degree-of-freedom models

    • Principal Investigator
      今泉 允聡
    • Project Period (FY)
      2024 – 2026
    • Research Category
      Grant-in-Aid for Scientific Research (B)
    • Review Section
      Basic Section 60030:Statistical science-related
      Basic Section 61030:Intelligent informatics-related
      Sections That Are Subject to Joint Review: Basic Section60030:Statistical science-related , Basic Section61030:Intelligent informatics-related
    • Research Institution
      The University of Tokyo
  •  Estimation of Stochastic Processes with Online Optimisation MethodsPrincipal Investigator

    • Principal Investigator
      Nakakita Shogo
    • Project Period (FY)
      2021 – 2022
    • Research Category
      Grant-in-Aid for Research Activity Start-up
    • Review Section
      0201:Algebra, geometry, analysis, applied mathematics,and related fields
    • Research Institution
      The University of Tokyo

All 2023 2022

All Presentation

  • [Presentation] Parametric estimation of ergodic diffusion processes by online gradient descent2023

    • Author(s)
      Shogo Nakakita
    • Organizer
      DYNSTOCH 2023 - Workshop on Statistical Methods for Dynamical Stochastic Models
    • Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-21K20318
  • [Presentation] Estimation of diffusion processes via online gradient descent2022

    • Author(s)
      Shogo Nakakita
    • Organizer
      15th International Conference of the European Consortium for Informatics and Mathematics Working Group on Computational and Methodological Statistics (CMStatistics 2022)
    • Invited / Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-21K20318
  • [Presentation] オンライン勾配降下法による確率微分方程式のパラメータ推定2022

    • Author(s)
      仲北祥悟
    • Organizer
      2022年度統計関連学会連合大会
    • Data Source
      KAKENHI-PROJECT-21K20318
  • 1.  今泉 允聡 (90814088)
    # of Collaborated Projects: 1 results
    # of Collaborated Products: 0 results
  • 2.  植松 良公 (40835279)
    # of Collaborated Projects: 1 results
    # of Collaborated Products: 0 results
  • 3.  矢田 和善 (90585803)
    # of Collaborated Projects: 1 results
    # of Collaborated Products: 0 results

URL: 

Are you sure that you want to link your ORCID iD to your KAKEN Researcher profile?
* This action can be performed only by the researcher himself/herself who is listed on the KAKEN Researcher’s page. Are you sure that this KAKEN Researcher’s page is your page?

この研究者とORCID iDの連携を行いますか?
※ この処理は、研究者本人だけが実行できます。

Information User Guide FAQ News Terms of Use Attribution of KAKENHI

Powered by NII kakenhi