BayesPy: Variational Bayesian Inference in Python']Python

被引:0
|
作者
Luttinen, Jaakko [1 ]
机构
[1] Aalto Univ, Dept Comp Sci, Espoo, Finland
关键词
variational Bayes; probabilistic programming; !text type='Python']Python[!/text;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
BayesPy is an open-source Python software package for performing variational Bayesian inference. It is based on the variational message passing framework and supports conjugate exponential family models. By removing the tedious task of implementing the variational Bayesian update equations, the user can construct models faster and in a less error-prone way. Simple syntax, flexible model construction and efficient inference make BayesPy suitable for both average and expert Bayesian users. It also supports some advanced methods such as stochastic and collapsed variational inference.
引用
收藏
页数:6
相关论文
共 50 条
  • [31] Bambi: A Simple Interface for Fitting Bayesian Linear Models in Python']Python
    Capretto, Tomas
    Piho, Camen
    Kumar, Ravin
    Westfall, Jacob
    Yarkoni, Tal
    Martin, Osvaldo A.
    JOURNAL OF STATISTICAL SOFTWARE, 2022, 103 (15): : 1 - 29
  • [32] PyDREAM: high-dimensional parameter inference for biological models in python']python
    Shockley, Erin M.
    Vrugt, Jasper A.
    Lopez, Carlos F.
    BIOINFORMATICS, 2018, 34 (04) : 695 - 697
  • [33] Hemodynamic effects of python']python neuropeptide γ in the anesthetized python']python, Python']Python regius
    Skovgaard, N
    Galli, G
    Taylor, EW
    Conlon, JM
    Wang, TB
    REGULATORY PEPTIDES, 2005, 128 (01) : 15 - 26
  • [34] Hemodynamic effects of python']python neuropeptide γ in the anaesthetized python']python, Python']Python regius
    Skovgarrd, N
    Galli, GLJ
    Taylor, EW
    Conlon, JM
    Wang, T
    COMPARATIVE BIOCHEMISTRY AND PHYSIOLOGY B-BIOCHEMISTRY & MOLECULAR BIOLOGY, 2004, 139 (01): : 148 - 149
  • [35] Formalizing Model Inference of MicroPython']Python
    de Ferro, Carlos Mao
    Cogumbreiro, Tiago
    Martins, Francisco
    2023 53RD ANNUAL IEEE/IFIP INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS AND NETWORKS WORKSHOPS, DSN-W, 2023, : 283 - 289
  • [36] HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python']Python
    Wiecki, Thomas V.
    Sofer, Imri
    Frank, Michael J.
    FRONTIERS IN NEUROINFORMATICS, 2013, 7
  • [37] ABC-SysBio-approximate Bayesian computation in Python']Python with GPU support
    Liepe, Juliane
    Barnes, Chris
    Cule, Erika
    Erguler, Kamil
    Kirk, Paul
    Toni, Tina
    Stumpf, Michael P. H.
    BIOINFORMATICS, 2010, 26 (14) : 1797 - 1799
  • [38] PyGpPHs: A Python']Python Package for Bayesian Modeling of Port-Hamiltonian Systems
    Li, Peilun
    Tan, Kaiyuan
    Beckers, Thomas
    IFAC PAPERSONLINE, 2024, 58 (06): : 54 - 59
  • [39] Bayesian Neural Networks via MCMC: A Python']Python-Based Tutorial
    Chandra, Rohitash
    Simmons, Joshua
    IEEE ACCESS, 2024, 12 : 70519 - 70549
  • [40] PySSM : APython']Python Module for Bayesian Inference of Linear Gaussian State Space Models
    Strickland, Christopher M.
    Burdett, Robert L.
    Mengersen, Kerrie L.
    Denham, Robert J.
    JOURNAL OF STATISTICAL SOFTWARE, 2014, 57 (06): : 1 - 37