Here we will maintain a repository of links to Bayesian and related web pages.

**Software.** Unless otherwise indicated these are computer programs for building and using Bayesian networks.

- Agena Risk
- BUGS (Bayesian inference Using Gibbs Sampling; free). Runs on Linux or Windows. This is not Bayesian network software, but does do Bayesian inference using stochastic sampling.
- Genie and Smile (free)
- Hugin Expert
- JavaBayes (free)
- Norsys, makers ofÂ Netica

**Information on Bayesian technology:**

- Bayesian Artificial Intelligence - research pages at Monash

**Bayes related blogs:**

I think the frequentist statistics have the advantages and disadvantages, the same as Bayesian stats. The freq stats are the most widely used because it make difficult problems and models tractable using scalar scstiatits, and made direct inferences that although relies strongly in asymptotic distribution provide an inference which everybody agrees in the result. The difficulty of model properly the prior distribution have as result additional discussion over this step of inference and not over the results of the inference. Also the freq def of probability is more intuitive and provide a clear meaning to statement p=0.68. A degree of belief is difficult to interpret and to compare with experiment. Also many difficulties in freq stats arise from the fact their method where develop at early 20 century. The lack of numerical power only make possible use simple statistics as mean, variance, kurtosis, etc. and compare the observed value with a table. The Bayesian statistics can not be properly compute the in the complicate cases, and only with the arising of tractable numerical aproximations is that the Bayesian methods become competitive. I think that with new research in more general and computational intensive frequentist inference methods many of the problems of this aproach can be resolve at least in part.To be fair I like some points of Bayesian statistics, specially the fact the probabilities are not related with inherent randomness but the ignorance of causes of phenomena. That make me more sense that an real randomness in nature. Also make the inference straightforward with Bayes theorem.I feel that both system have good points and weak points. May be in the future will be discover a new inference method which posses the characteristics of both system and will surpass them.

Of course, frequentism has advantages and disadvantages. Mostly the latter. For the former: their methods are fast and frugal.