Parameter inference for discretely observed stochastic kinetic models using stochastic gradient descent

@inproceedings{Wang2010ParameterIF,
  title={Parameter inference for discretely observed stochastic kinetic models using stochastic gradient descent},
  author={Yuanfeng Wang and Scott Christley and Eric Mjolsness and Xiaohui Xie},
  booktitle={BMC Systems Biology},
  year={2010}
}
Stochastic effects can be important for the behavior of processes involving small population numbers, so the study of stochastic models has become an important topic in the burgeoning field of computational systems biology. However analysis techniques for stochastic models have tended to lag behind their deterministic cousins due to the heavier computational demands of the statistical approaches for fitting the models to experimental data. There is a continuing need for more effective and… CONTINUE READING

Citations

Publications citing this paper.
Showing 1-10 of 29 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 54 references

Eds): Learning and Inference in Computational Systems Biology

  • ND Lawrence, M Girolami, M Rattray, G Sanguinetti
  • 2010
1 Excerpt

Bayesian Emulation and Calibration of a Stochastic Computer Model of Mitochondrial DNA Deletions in Substantia Nigra Neurons

  • DA Henderson, RJ Boys, KJ Krishnan, C Lawless, DJ Wilkinson
  • J Am Stat Assoc
  • 2009
2 Excerpts

Similar Papers

Loading similar papers…