This is where probability theory comes to our aid: estimate the true signals from noisy measurements in the presence of uncertainty. Bayesian inference has been widely applied in computational biology field. In certain systems for which we have a good understanding, i.e., gene regulation, behind...
Consider deep learning: you can train a network using Adam, RMSProp or a number of other optimizers. However, they tend to be rather similar to each other, all being variants of Stochastic Gradient Descent. In contrast, Bayesian methods of inference differ from each other more profoundly. The ...
As the Naive Bayesian model is highly dependent on the estimation of the conditional probability24, i.e., the prior probability has a large impact on the detection accuracy of the model, the prior knowledge matrix was employed to determine the hyperparameters in the prior distribution. We assume...
“Our paper presents, for the first time, a complete hardware implementation of a Bayesian neural network utilizing the intrinsic variability of memristors to store these probability distributions," said Elisa Vianello, CEA...
{H}_1\)). When the competing models are equally likely a priori, then the probability of making an error equals the size of the smaller area. Note that this kind of “error control” differs from that which is sought by classical statistics. In the Bayesian formulation the probability of...
Bayesian estimation of bandwidth in semiparametric kernel estimation of unknown probability mass and regression functions of count data. Comput. Statist. 2016, 31, 189–206. [Google Scholar] [CrossRef] [Green Version] Johnson, R.A.; Wichern, D.W. Applied Multivariate Statistical Analysis, 6th ...
Based on 1000 simulations, we computed Bayes estimates of α 1 , α 2 , θ 1 , θ 2 , γ 1 , and γ 2 along with 95 % CRIs and corresponding coverage probability (CP) using the MCMC method with 10,000 samples while discarding the first 1000 values as ’burn-in.’ We evaluated ...
Suppose the posterior distribution around the MAP point is rather “flat”; then, the probability is unchanged in a fairly large area around σ ∗ , meaning that all the configuration in this flat area can be approximately taken as the optimal point, and the reconstruction is insensitive to ...
This study introduces a unique flexible family of discrete probability distributions for modeling extreme count and zero-inflated count data with different failure rates. Certain significant mathematical properties, such as the cumulant generating function, moment generating function, dispersion index, L-mome...
This study introduces a unique flexible family of discrete probability distributions for modeling extreme count and zero-inflated count data with different failure rates. Certain significant mathematical properties, such as the cumulant generating function, moment generating function, dispersion index, L-mome...