"Warning: the CID values for both test machines are the same"

I came across a frustrating interesting problem today where the BizTalk Server 2006 Configuration wizard would fail every time I applied a new configuration.

Unlike a typical BizTalk developer environment (which usually consists of BizTalk and SQL Server on the same machine), this environment consisted of two separate machines: one BizTalk Server 2006 and one SQL Server 2005 (i.e. the BizTalk databases are stored on the SQL Server).  Additionally, this is a virtual environment and both machines were cloned from the same base Windows Server 2003 R2 template.

I was installing the following components ...

·         Enterprise Single Sign-On (SSO)

·         Group

·         BizTalk Runtime

·         MSMQT

It would successfully install ENTSSO, but would fail when installing the group.  The log file reported the following error:

Failed to configure with error message [Exception of type 'System.EnterpriseServices.TransactionProxyException' was thrown.]

The following Google search suggested to me that  the underlying problem was with MSDTC (aren't all BizTalk problems?).  I checked, and double-checked, the MSDTC properties on both servers and couldn't find anything wrong with the configuration.  So, I had to pull out the big guns.

I downloaded DTCPing (a very handy tool for debugging DTC issues) and ran it on both machines (make sure to read the instructions on how to use DTCPing as it is not straightforward).  In the generated log file I noticed the following warning:

WARNING: the CID values for both test machines are the same while this problem won't stop DTCping test, MSDTC will fail for this ...

A Google search on this warning helped me to understand that the underlying problem is that the CID values stored for MSDTC were not changed during the cloning process.  But of course!

If you're experiencing this problem, check the following registry key on both of your machines.  Are the keys identical?

HKEY_CLASSES_ROOT/CID

Mine were.  Here's the steps I took successfully reinstall MSDTC so that the CID values were unique.  Run this procedure on both machines:

1.     Use Add Windows Components, and remove Network DTC.

2.     Go to the command line and run: MSDTC -uninstall

3.     Go to the registry and delete the MSDTC keys in HKLM/Software/Microsoft/Software/MSDTC, HKLM/System/CurrentControlSet/Services/MSDTC, and HKEY_CLASSES_ROOT/CID (if they're still there).

4.     Reboot

5.     Go to the command line and run: MSDTC -install

6.     Use Add Windows Components, and add Network DTC.

7.     Go to the command line and run: net start msdtc

After running this on both servers I was able to confirm that the CID values were unique.  And, sure enough, when I next applied my configuration to BizTalk Server 2006 everything worked perfectly.

I hope this helps!

### 解决 JAGS 在使用 `runjags` 包时未提供初始值导致的警告问题 在使用 `runjags` 包运行 JAGS 模型时,如果未提供初始值,可能会出现警告信息,提示所有链使用相同的初始值[^1]。这种情况可能导致链之间的独立性不足,从而影响模型收敛的诊断。以下是解决该问题的方法: #### 方法 1:手动指定初始值 可以通过 `inits` 参数为每个链提供不同的初始值。以下是一个示例代码,展示如何为模型中的参数指定初始值: ```r # 定义初始值函数 initial_values <- function(chain) { list( beta0 = rnorm(1, mean = 0, sd = 10), # 截距的初始值 sigma.e = runif(1, min = 0.1, max = 10), # 组内标准差的初始值 sigma.b = runif(1, min = 0.1, max = 10) # 组间标准差的初始值 ) } # 运行 MCMC 分析,并传递初始值 results <- run.jags(model_string, data = data_list, monitor = c("beta0", "sigma.e", "sigma.b"), n.chains = 3, burnin = 1000, sample = 10000, inits = initial_values) ``` 上述代码中,`initial_values` 函数为每个链生成不同的随机初始值。通过这种方式,可以确保每个链从不同的起点开始,从而提高链的独立性[^2]。 #### 方法 2:使用 `gen.inits` 参数自动生成初始值 如果不想手动定义初始值,可以使用 `gen.inits = TRUE` 参数让 `runjags` 自动生成不同的初始值。例如: ```r # 运行 MCMC 分析,并启用自动初始值生成 results <- run.jags(model_string, data = data_list, monitor = c("beta0", "sigma.e", "sigma.b"), n.chains = 3, burnin = 1000, sample = 10000, gen.inits = TRUE) ``` 启用 `gen.inits = TRUE` 后,`runjags` 会为每个链生成不同的随机初始值,避免所有链使用相同初始值的问题[^3]。 #### 方法 3:调整模型参数以改善收敛性 如果警告仍然存在,可能需要检查模型的先验分布或数据结构是否合理。例如,可以尝试以下方法: - 使用更宽泛的先验分布以减少对初始值的敏感性。 - 增加迭代次数或烧录期以确保链充分收敛。 - 检查数据是否存在异常值或多重共线性问题。 #### 示例代码总结 以下是一个完整的代码示例,结合了手动指定初始值和自动生成初始值的方法: ```r # 加载 runjags 包 library(runjags) # 定义初始值函数 initial_values <- function(chain) { list( beta0 = rnorm(1, mean = 0, sd = 10), sigma.e = runif(1, min = 0.1, max = 10), sigma.b = runif(1, min = 0.1, max = 10) ) } # 运行 MCMC 分析 results <- run.jags(model_string, data = data_list, monitor = c("beta0", "sigma.e", "sigma.b"), n.chains = 3, burnin = 1000, sample = 10000, inits = initial_values) # 或者使用自动初始值生成 results <- run.jags(model_string, data = data_list, monitor = c("beta0", "sigma.e", "sigma.b"), n.chains = 3, burnin = 1000, sample = 10000, gen.inits = TRUE) ``` ### 注意事项 - 确保初始值在参数的合理范围内,避免模型难以收敛。 - 如果模型复杂度较高,建议优先使用手动指定初始值的方法以获得更好的控制。 - 在分析结果时,检查 Gelman-Rubin 统计量(Rhat)以确保链已充分收敛[^4]。
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