A theme for multiple comparisons problems under a frequentist paradigm is the need for an adjustment to control the overall error probability for the false detection of null effects. Our review will focus on Ba
An alternate approach involves considering multiple candidate models, M1,M2,…,MK, simultaneously to account for uncertainty at the model level [20–22]. Given a set of independent identically distributed (i.i.d.) data points y=(y1,…,yn), where yi∼p(yi|θk),i=1,…,n, where θk...
Hierarchical Multiple-Model Bayesian Approach to Transmural Electrophysiological Imaging Azar Rahimi, Jingjia Xu, and Linwei Wang Rochester Institute of Technology, Rochester, NY, 14623, USA Abstract. Noninvasive electrophysiological (EP) imaging of the heart aims to mathematically reconstruct the spatio...
In this section, we apply our BMRKR (Bayesian Multiple Response Kernel Regression) model on two simulated data sets and two real near infra-red spectroscopy data sets. Data pre-processing: The two real data sets are (i) Biscuit dough data (Osborne et al., 1984) and (ii) Wheat Data (...
The laser-induced incandescence (LII) method was used to acquire the 2D distribution of soot volume fraction, and an apparatus-independent yield sooting index (YSI) was experimentally obtained. Based on the database, a novel predicting model of YSI values for surrogate fuels was proposed with ...
.bayespredict (skewness:@skew({_ysim})) (kurtosis:@kurt {_ysim}), saving(ces_teststat) rseed(16)Computing predictions ... file ces_teststat.dta saved file ces_teststat.ster saved We named our predicted functions asskewnessandkurtosis. We can use these names to refer to the functions ...
However, our previous index can be defined in terms of these new indices, so there are only eight unique values in the set. The most important feature of these new indices is that, like the policy, they are defined moment-by-moment, but can be aggregated across multiple levels of ...
details in Supplementary InformationS4.6) indicated that multiple iterations were needed in order to approach global optima. Two Bayesian optimization models were built forPmandPr, respectively, owning to the distinctive two Bayesian optimization directions. We used the trained surrogate model to optimize...
Consider the multiple linear regression model that predicts the US real gross national product (GNPR) using a linear combination of industrial production index (IPI), total employment (E), and real wages (WR). GNPRt=β0+β1IPIt+β2Et+β3WRt+εt. For all t, εt is a series of indep...
apropositionrepresents the person's strength of belief that the proposition is true. It ranges from 0, which represents certainty that the proposition is false, to 1, which represents certainty that the proposition is true, and responds to evidence bearing on the proposition's truth.indexdecision...