Sentence examples for based on sample inference from inspiring English sources
Studying the bearing failure behavior led to the following observations: based on sample inference techniques, an excessive variation was observed in the parameters that characterize the failure distribution.
Data collection When conducting research based on surveys, probabilistic sampling of participants allows making inferences about population characteristics based on sample data.
The evaluation is based on sample-to-population inference: an observable sample is studied in order to learn about the unobservable population it represents.
A key advantage of variational Bayesian inference algorithms compared to inference algorithms based on sampling is the dramatic improvement in time complexity of the algorithm.
* Approximate inference schemes based on sampling (Monte Carlo) and deep neural networks allow rich models to scale up efficiently, and may also explain some of the algorithmic and neural underpinnings of human thought.
* Approximate probabilistic inference schemes based on sampling (Markov chain Monte Carlo, Sequential Monte Carlo (particle filtering)) and deep neural networks, and their use in modeling the dynamics of attention, online sentence processing, object recognition and multiple object tracking.
The hierarchical mixture model explicitly accounts for outlier expressions, and inferences are based on samples from posterior distributions generated from the Markov chain Monte Carlo algorithm we have developed.
Inferences were based on samples from the above posterior density obtained using a Gibbs sampler.
Current inferences are based on sampled populations from a wide geographical range covering the whole of the Indian Himalayas.
To study transcriptional regulatory modules of this dynamic metabolic process, we conducted gene regulation network analysis based on small-sample inference of graphical Gaussian model (GGM).
Sampling is based on Bayesian inference of a posterior parameter distribution (4) Pr θ | D = Pr D | θ * Pr θ where Pr(D| θ) is the probability of a parameter vector θ to describe the given data D and Pr is the prior probability of the parameters (see above).
