For the parameter estimation, Maximum likelihood estimation (MLE) method has been adopted. The proposed model has been applied to two sets of actual software failure data and it has been observed that the predicted failure times as per the proposed model are closer to the actual failure times....
The probability distribution p(θ|seq, M l ) can be calculated using Bayesian parameter estimation: $$p({\boldsymbol{\theta }}|{\bf{s}}{\bf{e}}{\bf{q}},{M}_{l})={C}^{-1}p({\bf{s}}{\bf{e}}{\bf{q}}|{\boldsymbol{\theta }},{M}_{l})p({\boldsymbol{\theta }}...
Others works have shown improvements thanks to Bayesian parameter estimation combined with traditional unfolding methods to reconstruct the neutron spectrum derived from the Bonner sphere count rates [3]. This paper investigates machine-learning methods using a reference micro-dosimeter [4], i.e., the...
This paper introduces a novel parameter estimation method for the probability tables of Bayesian network classifiers (BNCs), using hierarchical Dirichlet p
GemPy is an open-source, Python-based 3-D structural geological modeling software, which allows the implicit (i.e. automatic) creation of complex geological models from interface and orientation data. It also offers support for stochastic modeling to address parameter and model uncertainties. ...
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...
In order to formulate a Bayesian model, we put a Bernoulli distribution on each R ij with parameter σ(uiTvj) where σ is the logistic sigmoid function and u i , v j are the ith and jth columns of the respective factor matrices U∈RL×I and V∈RL×J. One can think of u i and...
We propose an alternative approach for the inference of time-varying parameter models. We exploit that many time series can be fitted by evaluating the contribution of each data point to the low-level parameter distribution in an iterative way, time step by time step. This allows us to breakdo...
In this paper, new classes of lower bounds on the outage error probability and on the mean-square-error (MSE) in Bayesian parameter estimation are proposed. The minima of the h-outage error probability and the MSE are obtained by the generalized maximum a-posteriori probability and the minimum...
1.01 749 1172#> zBase:Trt1 0.05 0.17 -0.30 0.38 1.00 833 1335#>#> Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS#> and Tail_ESS are effective sample size measures, and Rhat is the potential#> scale reduction factor on split chains (at convergence, Rhat = 1)...