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09-Oct-2017 12:25

According to Microsoft, Windows Server 2012 and Windows 8.1 automatically update Digital Certificates.Microsoft Security Advisory 3050995 https://technet.microsoft.com/library/security/3050995 says Server 2012 and Windows 8.1 don't need to do anything as they are automatically protected, however when checking the certificate stores on either there's no updates to the Untrusted Certificates/Certificate Trust List, which per that advisory should have added MCSHOLDING TEST.This is a 100% Server 2012 Essentials R2 / Windows 8.1 small biz location with no changes to OOB as regards certificates. Pretty concerning if the certs don't get updated automatically as MS claims they do. ^_^ Once again I have to apologize for not posting anything in a while.For one, it seems to be an opaque tool and I have yet to locate any means of adjusting how it behaves or even learn what criteria make files appear there.One thing I am sure of is that files which I have created in the last 24 hours (specifically, screen shots) do not appear there.How to update Clam AV whenever i try to update clam AV i get the following report "WARNING: Can't query WARNING: Invalid DNS reply. Flash Player 10 and later can use your system’s graphics hardware to accelerate video decoding.

If you have a prior $P(\theta)$ and a likelihood function $P(x \mid \theta)$ you can calculate the posterior with: $$ P(\theta \mid x) = \frac $$ Since $P(x)$ is just a normalization constant to make probabilities sum to one, you could write: $$P(\theta \mid x) \sim \sum_\theta P(x \mid \theta)P(\theta) $$ Where $\sim$ means "is proportional to." This Wikipedia article on conjugate priors may be informative. Let $P(\boldsymbol)$ be a prior over your parameters.I know, that the Chow-Test a useful test for model stability. How do we go about calculating a posterior with a prior N~(a, b) after observing n data points? Imagine that you want to estimate mean $\mu$ of normal distribution and $\sigma^2$ is known to you. We assume normal prior for $\mu$ with hyperparameters $\mu_0,\sigma_0^2:$ \begin X\mid\mu &\sim \mathrm(\mu,\ \sigma^2) \ \mu &\sim \mathrm(\mu_0,\ \sigma_0^2) \end Since normal distribution is a conjugate prior for $\mu$ of normal distribution, we have closed-form solution to update the prior \begin E(\mu' \mid x) &= \frac \[7pt] \mathrm(\mu' \mid x) &= \frac \end Unfortunately, such simple closed-form solutions are not available for more sophisticated problems and you have to rely on optimization algorithms (for point estimates using paper by Kevin P. Check also Do Bayesian priors become irrelevant with large sample size?I am working on redo search functionality on map drag event.I am using php as server side language, What I do in this is that when I refresh my code it shows all restaurants in listing and all these restaurants in google map with markers. for this I am using map dragend event, i call ajax function and fetch all restaurants with in the distance of 9 km from middle of the map.

If you have a prior $P(\theta)$ and a likelihood function $P(x \mid \theta)$ you can calculate the posterior with: $$ P(\theta \mid x) = \frac $$ Since $P(x)$ is just a normalization constant to make probabilities sum to one, you could write: $$P(\theta \mid x) \sim \sum_\theta P(x \mid \theta)P(\theta) $$ Where $\sim$ means "is proportional to." This Wikipedia article on conjugate priors may be informative. Let $P(\boldsymbol)$ be a prior over your parameters.I know, that the Chow-Test a useful test for model stability. How do we go about calculating a posterior with a prior N~(a, b) after observing n data points? Imagine that you want to estimate mean $\mu$ of normal distribution and $\sigma^2$ is known to you. We assume normal prior for $\mu$ with hyperparameters $\mu_0,\sigma_0^2:$ \begin X\mid\mu &\sim \mathrm(\mu,\ \sigma^2) \ \mu &\sim \mathrm(\mu_0,\ \sigma_0^2) \end Since normal distribution is a conjugate prior for $\mu$ of normal distribution, we have closed-form solution to update the prior \begin E(\mu' \mid x) &= \frac \[7pt] \mathrm(\mu' \mid x) &= \frac \end Unfortunately, such simple closed-form solutions are not available for more sophisticated problems and you have to rely on optimization algorithms (for point estimates using paper by Kevin P. Check also Do Bayesian priors become irrelevant with large sample size?I am working on redo search functionality on map drag event.I am using php as server side language, What I do in this is that when I refresh my code it shows all restaurants in listing and all these restaurants in google map with markers. for this I am using map dragend event, i call ajax function and fetch all restaurants with in the distance of 9 km from middle of the map.A new data point (of the next month) is available and I re-fit the model and obtain b = -3.5 and so on.