Bayesian Inference in the Presence of Intractable Normalizing Functions

@inproceedings{Park2017BayesianII,
  title={Bayesian Inference in the Presence of Intractable Normalizing Functions},
  author={Jaewoo Park and Murali Haran},
  year={2017}
}
  • Jaewoo Park, Murali Haran
  • Published 2017
  • Mathematics
  • ABSTRACTModels with intractable normalizing functions arise frequently in statistics. Common examples of such models include exponential random graph models for social networks and Markov point processes for ecology and disease modeling. Inference for these models is complicated because the normalizing functions of their probability distributions include the parameters of interest. In Bayesian analysis, they result in so-called doubly intractable posterior distributions which pose significant… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 55 REFERENCES

    Bayesian Analysis for Exponential Random Graph Models Using the Adaptive Exchange Sampler.

    VIEW 9 EXCERPTS
    HIGHLY INFLUENTIAL

    MCMC for Doubly-intractable Distributions

    VIEW 5 EXCERPTS
    HIGHLY INFLUENTIAL