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With Patch Wizard, you can determine patches that have not been applied to your system. It does not report on all available patches. It compares the patches you have already applied against a list of all recommended Oracle E- Business Suite patches. Here is Oracle's answer to question ' How do I know what changes xxx patch does to my system, what is impact' Patch Wizard is a Web-based utility in Oracle Applications Manager (OAM) Navigation : Login to System Administrator. Patch Tracking Utilities. It is important to keep track of the patching status of your system. To access Patch Wizard, log in to Oracle Applications Manager (OAM) and choose Patch Wizard from the Navigator pane. Oracle E-Business Suite Patching Procedures Release 12.1. Select View instance-specific instructions for a new patch. Enter the APPS password. To download patches using Patch Wizard. Access Oracle Applications Manager. NAME: PATCH WIZARD (FNDLOAD) SHORT CODE: PAFNDLOAD MODULE: Oracle Applications DBA Description: Navigation: Oracle Applications DBA. Oracle patch tracking & patch management : Search BC Oracle Sites. Apps Support Analysis Design. See at the patch log file at $ORACLE Oracle R12.1.3 patch impact with Patch Wizard Patch Wizard Description. Recommend/Analyze Patches fails with Ora-06512: At 'Apps.Ad. Recommended patches can include high- priority patches or patches that update to a new codelevel, such as release update packs (RUPs), product family RUPs, and pre- upgrade patches. How Patch Wizard Works. Patch Wizard supplies you with an interface from which you can: Set preferences, both site- specific and general, that include the staging directory and various defaults that will apply to the patches you download. Set up filters that report only those patches that may affect your system. Submit a request for a report of recommended patches, based on the filter(s) you set up. Analyze the impact of specific patches from a list you supply to Patch Wizard. Download patches, specify the language of the patches to download, and merge patches. Before running any of the Patch Wizard tasks, set up your My Oracle Support (Metalink) credentials on the OAM Update Metalink Credentials page. To access this page, go to the Patch Wizard main page, click Setup in the title bar. Alternatively, click the Setup link at the top right of any page of the OAM interface. Management Agent Release Notes for Microsoft Windows (64-bit) on Intel Itanium. 10 g Release 5 (10.2.0.5) E14916-03. Oracle Enterprise Manager Grid Control is a management. Patch Set Upgrade Of Windows Agent Sharing Oracle Home Fails From Patch Wizard. Oracle EBS Release 12: Tips for Patching 13,176 views. Inside the Patch Wizard The Dashboard Setup page appears. Click the Metalink Credentials link on the left side. The Update Metalink Credentials page appears. OAM Update Metalink Credentials Page. Update your My Oracle Support credentials by providing your user ID, password, email address, proxy server host name, proxy server port, proxy bypass domains, proxy user name, and proxy password. The recommend, analyze, and download patches features typically require that your My Oracle Support user ID and password are set on the OAM Update Metalink Credentials page. However, the recommend and analyze features can also be used when Patch Wizard does not have access to a direct Internet connection. If this is the case, you can leave the Metalink user ID and password empty. The Patch Information Bundle. The Patch Information Bundle file contains the zip files of recommended patches, the list of recommended patches (Recommended. Codelevels. xml), and information on products and product families (Product. Info. xml). Each patch zip file contains a readme file, a patch LDT file, and a patch metadata file (patch. When you submit a patch analysis request, this file is automatically downloaded (if it is not specified otherwise in your Metalink credentials.) Patch Wizard loads the Patch Information Bundle data, including LDT files and readme files, into the Oracle E- Business Suite database. It uses the metadata to provide patch recommendations. Concurrent Programs. When you submit a request for patch analysis, Patch Wizard performs the following tasks using a set of concurrent programs: Uploading patch information from the Patch Information Bundle to Patch Wizard tables. Patch Wizard loads the Patch Information Bundle metadata, including LDT files and readme files, into the Oracle E- Business Suite database. Recommending patches based on the current environment and the Patch Information Bundle. Patch Wizard reports which patches update Oracle E- Business Suite at the current codelevel and which update to a new codelevel. Downloading patches (ad hoc or based on the list of recommended patches)Patch Wizard can download patches from My Oracle Support, and then merge the patches in the Patch Wizard staging directory. Analyzing lists of patches after downloading them from My Oracle Support. Patch Wizard uploads the metadata for a specific patch or set of patches for you to view information reported from the metadata. For example, you can submit a request for patch analysis, and then view any recommended patches that have not yet been applied and the impact of applying this new patch. Running Patch Wizard Without Access to an Internet Connection. You can run Patch Wizard without access to an Internet connection, if necessary, by downloading the Patch Information Bundle to a system which has Internet access. Once the download is complete, copy the Patch Information Bundle file to the Patch Wizard's staging directory. Then run Patch Wizard as you normally would, to recommend and analyze patches, based on the files you copied to the staging directory. The Patch Wizard Interface. Patch Wizard is a Web- based utility in Oracle Applications Manager (OAM). The OAM interface gives Patch Wizard pages a uniform look and feel. See: OAM Interface, Oracle E- Business Suite Maintenance Utilities. Main Page From this page, you have access to task icons used to set up the Patch Wizard staging directory, manage patch filters, submit concurrent requests, and view recommended patches. In addition, the Recommended Results section of this page displays a list of patches based on submitted requests. Task Icons From the main page, access the other Patch Wizard pages by clicking on Task icons. The icons provide links to the following pages: Patch Wizard Preferences, Define Patch Filters, Recommended/Analyze Patches, Download Patches, and Aggregate Patch Impact. Details Icons. On many Patch Wizard pages, you can drill down to see more detail. For example, from the Recommended Patches Results section of the main page, click the Details icon for a specific recommended patch request to view the recommended patch results. Accessing Patch Wizard. To access Patch Wizard, log in to Oracle Applications Manager (OAM) and choose Patch Wizard from the Navigator pane. Enter the following URL in your browser: http: //< HTTP hostname>.< domain> :< HTTP port> /OA. The Oracle E- Business Suite Home page appears. Click the System Administration link in the Navigator pane. Another Navigator pane appears to the right. Go to the Patch Wizard main page. Scroll down to the Oracle Applications Manager section of the right- hand Navigator pane, and click Patch Wizard to go to the main page. Patch Wizard Main Page. Use the main page to access all features of Patch Wizard and to view the results of your requests for recommended patches. The Select Feature drop- down list at the top of the page provides access to the Applied Patches, File History, Timing Reports, and Register Flagged Files features. Patch Wizard Main Page. Patch Wizard Tasks Table. The Patch Wizard Tasks table lists the tasks available in Patch Wizard. The table contains the following columns of information for each task: Task Name: Name of the Patch Wizard task. Description: Describes the task. Tasks: Link to the page associated with the Patch Wizard task. Job Status: Link to the request submission status of the task where you can review warnings or errors for your request. From the Patch Wizard Tasks table, choose the Tasks icons to view: The Patch Wizard Preferences page. From the Preferences page, set the staging directory, merge patch defaults, the languages and platform defaults for downloading patches, and whether to display or hide hidden patches. You must define the values on this page before you use any other Patch Wizard feature. You can also use this page to modify existing preferences at a later date. The Define Patch Filters page. Typically, you see only those patches that are recommended for your system. Use this page to set up filters that report only those patches that may affect your system. The Recommend/Analyze Patches page. Once you have selected values for a filter, submit the request to create a report of recommended patches based on that filter. You can also analyze specific patches by entering a comma- separated list of patch numbers. The Download Patches page. You can download patches, specify the language of patches to download, and merge patches from this page. Recommended Patches Results. The Recommended Patches Results section of the Patch Wizard main page lists all in- progress and completed requests for patch recommendations, based on the information you entered in the Filter Criteria section of the main page. For example, you can view only the results that contain a certain text string in the filter name, or only the results of requests completed on a certain date. Patch Wizard Main Page - Recommended Patches Results. Clicking the icon in the Details column of a specific recommended patch request accesses the Recommended Patches Results page. After setting up and submitting a request, view the details of the recommended patches on this page. Patch Wizard Preferences. The site- specific information you set on the Patch Wizard Preferences page applies to other functions of Patch Wizard, such as Recommend/Analyze, Download Patches, and Codelevels Summary. From the Patch Wizard main page, click the Tasks icon for Patch Wizard Preferences. The top portion of the page contains these sections: Staging Directory, Merge Option Defaults, and Language and Platform Details. Patch Wizard Preferences Page - Top. The bottom portion of the page contains the In Use Products Defaults and the Display Option Defaults sections. Patch Wizard Preferences Page - Bottom. Staging Directory. Depending on the product type (AD or non- AD), Patch Wizard downloads patches to a subdirectory under the staging directory. If the patch that ADOAM is trying to download is an AD product patch, it will be downloaded to the . Non- AD Patches will be downloaded to “nonad. These temporary files and directories are deleted after processing. Note: Oracle recommends you use the same staging directory each time you run Patch Wizard. Merge Option Defaults. Merging patches reduces patch application time by eliminating redundant tasks (such as responding to prompts and linking executables) for each individual patch. We offer the Guaranteed Sales Program because we are confident that we have the staff, tools, and resources to sell your house. Century 21 Diversified 8310 Allison Pointe Bl. Indianapolis, IN 26250 (317) 585-7748.
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Robust statistics - Wikipedia, the free encyclopedia. Robust statistics are statistics with good performance for data drawn from a wide range of probability distributions, especially for distributions that are not normal. Robust statistical methods have been developed for many common problems, such as estimating location, scale and regression parameters. One motivation is to produce statistical methods that are not unduly affected by outliers. Another motivation is to provide methods with good performance when there are small departures from parametric distributions. For example, robust methods work well for mixtures of two normal distributions with different standard- deviations; under this model, non- robust methods like a t- test work badly. Introduction. In statistics, classical estimation methods rely heavily on assumptions which are often not met in practice. In particular, it is often assumed that the data errors are normally distributed, at least approximately, or that the central limit theorem can be relied on to produce normally distributed estimates. Unfortunately, when there are outliers in the data, classical estimators often have very poor performance, when judged using the breakdown point and the influence function, described below. The interest in nonparametric statistical analysis has grown recently in the field of computational intelligence. In many experimental studies, the lack of the required properties for a proper application. The practical effect of problems seen in the influence function can be studied empirically by examining the sampling distribution of proposed estimators under a mixture model, where one mixes in a small amount (1. For instance, one may use a mixture of 9. Robust parametric statistics can proceed in two ways: by designing estimators so that a pre- selected behaviour of the influence function is achievedby replacing estimators that are optimal under the assumption of a normal distribution with estimators that are optimal for, or at least derived for, other distributions: for example using the t- distribution with low degrees of freedom (high kurtosis; degrees of freedom between 4 and 6 have often been found to be useful in practice. L- estimators are a general class of simple statistics, often robust, while M- estimators are a general class of robust statistics, and are now the preferred solution, though they can be quite involved to calculate. Definition. This means that if the assumptions are only approximately met, the robust estimator will still have a reasonable efficiency, and reasonably small bias, as well as being asymptotically unbiased, meaning having a bias tending towards 0 as the sample size tends towards infinity. One of the most important cases is distributional robustness. Thus, in the context of robust statistics, distributionally robust and outlier- resistant are effectively synonymous. The data sets for that book can be found via the Classic data sets page, and the book's website contains more information on the data. Although the bulk of the data look to be more or less normally distributed, there are two obvious outliers. These outliers have a large effect on the mean, dragging it towards them, and away from the center of the bulk of the data. Thus, if the mean is intended as a measure of the location of the center of the data, it is, in a sense, biased when outliers are present. Also, the distribution of the mean is known to be asymptotically normal due to the central limit theorem. However, outliers can make the distribution of the mean non- normal even for fairly large data sets. Besides this non- normality, the mean is also inefficient in the presence of outliers and less variable measures of location are available. Estimation of location. Also shown is a normal Q. The outliers are clearly visible in these plots. Panels (c) and (d) of the plot show the bootstrap distribution of the mean (c) and the 1. The trimmed mean is a simple robust estimator of location that deletes a certain percentage of observations (1. The analysis was performed in R and 1. The distribution of the mean is clearly much wider than that of the 1. Also note that whereas the distribution of the trimmed mean appears to be close to normal, the distribution of the raw mean is quite skewed to the left. So, in this sample of 6. Robust statistical methods, of which the trimmed mean is a simple example, seek to outperform classical statistical methods in the presence of outliers, or, more generally, when underlying parametric assumptions are not quite correct. Whilst the trimmed mean performs well relative to the mean in this example, better robust estimates are available. In fact, the mean, median and trimmed mean are all special cases of M- estimators. Details appear in the sections below. Estimation of scale. The plots are based on 1. Gaussian noise added to the resampled data (smoothed bootstrap). Panel (a) shows the distribution of the standard deviation, (b) of the MAD and (c) of Qn. The distribution of standard deviation is erratic and wide, a result of the outliers. The MAD is better behaved, and Qn is a little bit more efficient than MAD. This simple example demonstrates that when outliers are present, the standard deviation cannot be recommended as an estimate of scale. Manual screening for outliers. Indeed, in the speed- of- light example above, it is easy to see and remove the two outliers prior to proceeding with any further analysis. However, in modern times, data sets often consist of large numbers of variables being measured on large numbers of experimental units. Therefore, manual screening for outliers is often impractical. Outliers can often interact in such a way that they mask each other. As a simple example, consider a small univariate data set containing one modest and one large outlier. The estimated standard deviation will be grossly inflated by the large outlier. The result is that the modest outlier looks relatively normal. As soon as the large outlier is removed, the estimated standard deviation shrinks, and the modest outlier now looks unusual. This problem of masking gets worse as the complexity of the data increases. For example, in regression problems, diagnostic plots are used to identify outliers. However, it is common that once a few outliers have been removed, others become visible. The problem is even worse in higher dimensions. Robust methods provide automatic ways of detecting, downweighting (or removing), and flagging outliers, largely removing the need for manual screening. Care must be taken; initial data showing the ozone hole first appearing over Antarctica were rejected as outliers by non- human screening. Such an estimator has a breakdown point of 0 because we can make x. Intuitively, we can understand that a breakdown point cannot exceed 5. Therefore, the maximum breakdown point is 0. For example, the median has a breakdown point of 0. The X% trimmed mean has breakdown point of X%, for the chosen level of X. Huber (1. 98. 1) and Maronna, Martin & Yohai (2. The level and the power breakdown points of tests are investigated in He, Simpson & Portnoy (1. Statistics with high breakdown points are sometimes called resistant statistics. The estimate of scale produced by the Qn method is 6. We can divide this by the square root of the sample size to get a robust standard error, and we find this quantity to be 0. Thus, the change in the mean resulting from removing two outliers is approximately twice the robust standard error. The 1. 0% trimmed mean for the speed- of- light data is 2. Removing the two lowest observations and recomputing gives 2. Clearly, the trimmed mean is less affected by the outliers and has a higher breakdown point. Notice that if we replace the lowest observation, . In many areas of applied statistics, it is common for data to be log- transformed to make them near symmetrical. Very small values become large negative when log- transformed, and zeroes become negatively infinite. Therefore, this example is of practical interest. Empirical influence function. It is a model- free measure in the sense that it simply relies on calculating the estimator again with a different sample. On the right is Tukey's biweight function, which, as we will later see, is an example of what a . The empirical influence function EIFi. Alternatively, the EIF is defined as the (scaled by n+1 instead of n) effect on the estimator of adding the point x. The approach is quite different from that of the previous paragraph. What we are now trying to do is to see what happens to an estimator when we change the distribution of the data slightly: it assumes a distribution, and measures sensitivity to change in this distribution. By contrast, the empirical influence assumes a sample set, and measures sensitivity to change in the samples. Let A. We want to estimate the parameter . Let the functional T: A. We will suppose that this functional is Fisher consistent, i. This means that at the model F. What happens when the data doesn't follow the model F. The influence function is then defined by: IF(x; T; F): =limt. For a robust estimator, we want a bounded influence function, that is, one which does not go to infinity as x becomes arbitrarily large. Desirable properties. However, M- estimators now appear to dominate the field as a result of their generality, high breakdown point, and their efficiency. See Huber (1. 98. M- estimators are a generalization of maximum likelihood estimators (MLEs). What we try to do with MLE's is to maximize . In 1. 96. 4, Huber proposed to generalize this to the minimization of . MLE are therefore a special case of M- estimators (hence the name: . The two figures below show four . When Winsorizing is used, a mixture of these two effects is introduced: for small values of x, . This Winsorised estimator is also known as the Huber loss function. Tukey's biweight (also known as bisquare) function behaves in a similar way to the squared error function at first, but for larger errors, the function tapers off. Properties of M- estimators. 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Telugu wedding ceremony - Wikipedia, the free encyclopedia. The Telugu wedding ceremony (Telugu: . In the 1. 9th century, the ceremony could last up to 1. Padahaaru Rojula Panduga). In modern times, it can last 5 or more days, depending on the family's financial and social status. Vivaham or Wedding considered the strongest of social bonds, is said to spiritually merge two souls opening the doors to . Symbolic gestures and rituals encompass the ceremony and ensure that the bride and groom are united in the presence of Panchabhutaalu (Five essential elements for life) namely Bhumi (Earth), Akaasham (Sky), Agni (Fire), Neeru (Water) and Vaayuvu (air). The ceremony is held under the Ka. This process is called . Nowadays this tradition is fading out. After every ceremony they serve food to all the guests which is also the main part of the culture of offering food to anyone who comes on an auspicious day . All the rituals conducted throughout the Telugu wedding ceremony hold religious significance. Each element in the ceremonies is connected with the other and is given special importance. The decorations mostly consist of flowers and mango leaves in rich colors. The families renovate their houses and invite all the guests going to each of their houses with the kumkuma. It is also a tradition to eat ice cream or sweets after dinner because it is considered auspicious. Pre- wedding customs. Almost all festivals are celebrated with religious observances, holding supreme importance in the lives of the residents of the state. Ni. The two families meet to perform rituals to make the engagement official. The Telugu people generally avoid the months or a time period where Aashaadham, Bhadrapadam and Shunya maasam occurs, because they are considered inauspicious for the ceremonies. The couple is then blessed by elders of both families, and is given gifts including jewelry and clothing by their new family. During this ceremony the future mother- in- law of the bride presents her with clothes, gold and also silverware, formally ending the engagement (Ni. It is usually performed before householder responsibilities are handed over to the Groom. As a part of this custom, the groom is asked to wear a silver thread on his body. The ritual is conducted a few hours prior to the wedding. K. However, as a pre- wedding ceremony, the groom just pretends to go to Kashi and says that he has discarded the worldly pleasures (such as marriage, relations and properties) and is no more interested in leading a family life. He will then be stopped by the brother(cousins) of the bride, who persuade him to assume the responsibility of a house hold as in they tease each other quite a lot and groom readily agrees for it in the end. Wedding customs . In Andhra Pradesh, the Telugu people follow their own traditions, while conduction a wedding. Unlike other South Indian weddings, the muh. Telugu Brahmin wedding customs differ from the wedding customs of the other Telugu communities. In addition to the rituals mentioned below, their weddings start with rituals common in South Indian Brahmin weddings like Punyahavachanam, Ni. The aim is to purify them and make them prepared to perform sacred rites. A number of rituals are performed in an Indian wedding. Check out the wedding customs in India. Catholic Wedding Program. Religious elements or traditions can enrich a wedding ceremony, but determining what to include in the program to reflect your beliefs as a couple can be difficult. Reform Jewish Wedding Program.This bath is called Abhyangana Sn. This is done to cleanse their skin, so that a natural glow is radiated after they bathe. This is where the actual ceremony starts. They are given a new set of clothing to wear and are blessed that everything goes by well in the preparation. The bride is told not to go out of the town until the actual wedding ceremony. Aarti or H. Thereafter, the family members get together to perform Aarti. They pray for the bride and groom to be granted the wisdom to lead their life happily. And the bride does the Gauri P. It is during this time that . Elderly couples from both families are required to attend and witness the . During the ceremony, the bride sits in a bamboo basket. Her maternal uncle brings her to the ma. Until the completion of the Kany. Thereafter, the bride. The groom is made to chant . The groom holds the hand of the bride. The Mantras say: The Devas have offered you to me in order that I may live the life of a householder (Gruhasta); we shall not part from each other. J. Thereafter, the couple is asked to place a paste made from cumin seeds and jaggery on each other. This custom is referred to as J. This ceremony is observed to communicate that the relationship of the married couple is unbreakable and inseparable. This is the actual muh. The ritual of changing Bride's Gotram, . Henceforth, the bride no more belongs to the father's gotra and belongs to Groom's gotra. As a part of Madhuparka. The groom wears a white dhoti with a red border. White symbolizes purity and red represents strength. Sumanga. Six out of the ten women hold plates containing sacred rice(a mixture of rice and turmeric powder), while the rest of the four hold small lit lamps on their respective plates. Rice represents abundance, while the lit lamps symbolize light. Ma. In order to perform the ritual, the partition between the bride and groom are removed. After removing the partition, the groom ties the two strings of the Ma. In the Telugu wedding, the groom ties three knots of Ma. Married people witnessing this occasion come forward to bless the couple, by sprinkling flower petals and rice coated with turmeric powder. Saptapadi (. During this the pallu (edge of the sari) of the bride. This also is believed that the man bends to the woman in order to claim her as his. Also in order to ward- off the evil eye, the bride is adorned by a string of black beads during the ceremony. These beads along with the silver toe rings symbolize that she is a married woman. The groom puts his right hand in and the bride puts her left hand in and they fish for the ring. They do this three times and whoever wins more is supposed to be the dominant one in the marriage. This is a time of fun, because water splashes everywhere and there are chants and shouts of support for either side. Also, the bride is made to cook ( a namesake meal) on the sacred flame of the Agnih. This is when the bride is traditionally handed off to the groom and his family. Post- wedding customs. She is given a hearty welcome by the groom. As she steps into her new home, she is welcomed by the groom. This puja is first mentioned in Skanda Purana, Reva Kan. The details are part of the Katha/. As a customary, the Telugu speaking people unite the two Ma. This ritual is done sixteen days post wedding. This ritual can be performed by either the groom or an elderly member of the family. A few black or golden beads are slipped between the two 'plates' of the Ma. After the ceremony is over, the bride takes a bath and wears a new sari. |
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