Posterior Analysis for Normalized Random Measures with Independent Increments
By Lancelot F. James, Antonio Lijoi, Igor Prünster. 2009
235 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/88eeab2664636aa2939da5642e21fb6e49198bc5
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Igor Prünster
- Antonio Lijoi
- Mark F. J. Steel
- S. Favaro
- Bernardo Nipoti
- Jim E. Griffin
- Mathieu Gerber
- Dahua Lin
In Statistics and Probability
- Mark F. J. Steel
- S. Favaro
- Antonio Lijoi
- Igor Prünster
- Jim E. Griffin
- Bernardo Nipoti
- Dahua Lin
- Mathieu Gerber
Citations in the corpus
- Bayesian inference with dependent normalized completely random measures (2014, methodology): “Such a procedure has then been extended and analyzed for general univariate CRMs in [18,19,36].”
- Sequential quasi Monte Carlo (2015, background): “Note thatmt(x1:t−1,xt) is also available in closed-form expression for broader classes of random probability measures including the two-parameter Poisson–Dirichlet process (Pitman and Yor, 1997) and normalised random measures with independent increments (Regazzini et al., 2003; James et al., 2009).”
- Stick-breaking autoregressive processes (2011, methodology): “…in economics include autoregressive panel data models (Hirano, 2002), longitudinal data treatment models (Chib and Hamilton, 2002) and stochastic frontiers (Griffin and Steel, 2004). and James, 2001, 2003) (which will be discussed in section 2), and Normalized Random Measures (James et al., 2009).”
- On the stick-breaking representation of normalized inverse Gaussian priors (2012, methodology): “By now many properties such as finite-dimensional, predictive and posterior distributions are known [19, 14, 15].”
- Online Learning of Nonparametric Mixture Models via Sequential Variational Approximation (2013, background): “It is an interesting future direction to investigate how it can be generalized to a broader family of BNP models, such as HDP, Pitman-Yor processes, and NRMIs [10].”
- Streaming Variational Inference for Bayesian Nonparametric Mixture Models (2014, background): “However, since T 6= 1 in general, CRMs cannot directly be used as priors for mixture models.”