IndexingSignalAggregatorRunningMeanAndVarianceInternalState

Indexing SignalIndexing

GoogleApi.ContentWarehouse.V1.Model.IndexingSignalAggregatorRunningMeanAndVarianceInternalState

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SEO Impact
Internal state of the West & Chan running variance algorithm. Fields of this proto should not be accessed directly; instead, please use RunningMeanAndVarianceUtil. The fields of this message only have meaning in the context of the West & Chan algorithm, which is documented (or Wikipedia-linked) in the doc comments of RunningMeanAndVarianceUtil. We do however give some explanation of the meanings of these fields in the context of the algorithm (i.e. if you have the Wikipedia page open and are ready to do some math). Also see the file doc of RunningMeanAndVarianceUtil for a specification and more info about the algorithm. Notation: The data set is X = {(x_1, w_1), ..., (x_n, w_n)}. It consists of n weighted data points. The ith data point has value x_i and weight w_i. REQUIRES: x_i is finite for each i. w_i is finite for each i. w_i >= 0 for each i.

SEO Analysis

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Controls how pages are indexed. Without proper indexing, pages cannot appear in search results at all. This model (Indexing Signal Aggregator Running Mean And Variance Internal State) contains SEO-relevant attributes including totalWeight. Key functionality includes: The variable which in the Wikipedia page is referred to as M_2: m2 = w_1 (x_1 - mean)^2 + ... + w_n (x_n - mean)^2. The algorithm implemented in Run...

Actionable Insights for SEOs

  • Monitor for changes in rankings that may correlate with updates to this system
  • Consider how your content strategy aligns with what this signal evaluates
  • Optimize crawl budget by fixing broken links and reducing redirect chains
  • Use robots.txt and sitemap.xml effectively to guide crawling
  • Monitor Google Search Console for crawl errors and indexing issues

Attributes

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m2float(
Default: nil

The variable which in the Wikipedia page is referred to as M_2: m2 = w_1 (x_1 - mean)^2 + ... + w_n (x_n - mean)^2. The algorithm implemented in RunningMeanAndVarianceUtil provides a way to update m2 in a numerically stable way when the data set grows. If total_weight = 0, then m2 is meaningless, and its value is unspecified, except that it must be finite and >= 0.

meanfloat(
Default: nil

Mean of the data set, mean = (w_1 x_1 + ... + w_n x_n) / total_weight. The algorithm implemented in RunningMeanAndVarianceUtil provides a way to update this mean in a numerically stable way when the data set grows. If total_weight = 0, then mean is meaningless, and its value is unspecified, except that it must be finite.

totalWeightfloat(
Default: nil

Total weight of the data set, total_weight = w_1 + ... + w_n.