Estimation and Inference via Bayesian Simulation: An Introduction to Markov Chain Monte Carlo
By S. Jackman. 2000
275 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/72d91caa7d15bb864405edd7a8ed905b197f0ec3
How researchers see this paper
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In Artificial Intelligence
In Statistics and Probability
- Jeffrey B. Lewis
- Justin Grimmer
- K. Poole
- Howard Rosenthal
- C. D. Vries
- Daniel L. Oberski
- Christopher H. Achen
- R. Franzese
Citations in the corpus
- Making Their Mark: The Effects of Neighbourhood and Upper Secondary School on Educational Achievement (2008, unknown): “As noted by Jackman (2000),”
- Measuring Bias and Uncertainty in Ideal Point Estimates via the Parametric Bootstrap (2004, background): “115Ideal Point Estimation with Parametric Bootstrap and other quantities of interest can be computed (see Jackman 2000a, 2000b, 2001).”
- An Introduction to Bayesian Inference via Variational Approximations (2011, methodology): “But to compare variational approximations to Gibbs samplers and EM algorithms, we apply a variational approximation to a standard Bayesian probit regression model (Jackman 2000).”