Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters. BMC Bioinformatics, 14(252), 2013b. doi: doi:10.1186/ 1471-2105-14-252.↵ Hensman J. , et al . ( 2013 ) Hierarchical Bayesian modelling of gene expression time series across ...
Hierarchical Bayesian modelling provides an effective Swiss army knife for the analysis of single-cell data because of its ability to decompose variance and quantify uncertainty even in the face of sparse, high-dimensional data. Not surprisingly, Bayesian models are being developed in a variety of ...
Kabuki is a Python library intended to make hierarchical PyMC models reusable, portable and more flexible. Once a model has been formulated in kabuki it is trivial to apply it to new datasets in various ways. Currently, it is geared towards hierarchical Bayesian models that are common in the ...
HDDM is a python toolbox for hierarchical Bayesian parameter estimation of the Drift Diffusion Model (via PyMC). Drift Diffusion Models are used widely in psychology and cognitive neuroscience to study decision making. Check out thetutorialon how to get started. Further information can be found be...
后续Mathys还有K Stepahn以及Friston等人提出了分层高斯滤波器(hierarchical gaussian filter,HGF)试图解决。不过在笔者看来hgf似乎只是用变分推断近似得到了模型的解析解,但是这一解析解看起来仍很抽象,很难想象大脑是通过如何的方法实现这些具体计算的。 由这篇研究延伸开来,许多研究均发现人类和动物的智能对环境的灵活...
The accurate calibration of semi-empirical fatigue models against experimental evidence is a critical step for achieving reliable predictions. Amongst many semi-empirical fatigue models, El Haddad’s (EH) curve is widely exploited to characterise the fat
However, there might be some justification in criticism that these high-dimension models also tend to be over- parameterized and thus too flexible. One approach would be do a hierarchical Bayesian analysis [63] to constrain parameter sets in order to prevent the problem of over- fitting and ...
Hierarchical models for Bayesian inference—indeed, one huge advantage of using Bayesian is the ability to perform hierarchical modelling. This is an area that has garnered great interest and detailed research in this area will add to the body of knowledge for Bayesian and its modern implementation...
Bayesian Population Analysis using WinBUGS: A Hierarchical Perspective (Academic, 2011). McCarthy, M. Bayesian Methods of Ecology 5th edn (Cambridge Univ. Press, 2012). Korner-Nievergelt, F. et al. Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan (Academic, 2015...
We will eventually discuss robust regression and hierarchical linear models, a powerful modelling technique made tractable by rapid MCMC implementations. From a quantitative finance point of view we will also take a look at a stochastic volatility model using PyMC and see how we can use this model...