Titlebook: Bayesian Computation with R; Jim Albert Textbook 2009Latest edition Springer-Verlag New York 2009 Bayesian Inference.Hierarchical modeling

開脫

書目名稱Bayesian Computation with R影響因子(影響力)




書目名稱Bayesian Computation with R影響因子(影響力)學科排名




書目名稱Bayesian Computation with R網(wǎng)絡(luò)公開度




書目名稱Bayesian Computation with R網(wǎng)絡(luò)公開度學科排名




書目名稱Bayesian Computation with R被引頻次




書目名稱Bayesian Computation with R被引頻次學科排名




書目名稱Bayesian Computation with R年度引用




書目名稱Bayesian Computation with R年度引用學科排名




書目名稱Bayesian Computation with R讀者反饋




書目名稱Bayesian Computation with R讀者反饋學科排名

失誤

Introduction to Bayesian Thinking,rtion. Before taking data, one has beliefs about the value of the proportion and one models his or her beliefs in terms of a prior distribution. We will illustrate the use of different functional forms for this prior. After data have been observed, one updates one’s beliefs about the proportion by c

修飾語

Single-Parameter Models,sian inference for a variance for a normal population and inference for a Poisson mean when informative prior information is available. For both problems, summarization of the posterior distribution is facilitated by the use of R functions to compute and simulate distributions from the exponential f

Diluge

膽小鬼

Markov Chain Monte Carlo Methods,ior distribution, but it can be difficult to set up since it requires the construction of a suitable proposal density. Importance sampling and SIR algorithms are also general-purpose algorithms, but they also require proposal densities that may be difficult to find for high-dimensional problems. In

harpsichord

蒸發(fā)

OREX

SEED

Hot-Flash