Causal analysis is the field of experimental design and statistics pertaining to establishing cause and effect. Typically, it involves establishing four elements: correlation, sequence in time (that is, causes must occur before their proposed effect), a plausible physical or information-theoretical mechanism for an observed effect to follow from a possible cause, and eliminating the possibility of common and alternative (“special”) causes. Such analysis usually involves one or more artificial or natural experiments.
Bayes’ theorem (alternatively Bayes’ law or Bayes’ rule, after Thomas Bayes) provides a mathematical rule. It is used for inverting conditional probabilities. This enables us to find the probability of a cause given its effect.[1] For example, we know the risk of developing health problems increases with age. Bayes’ theorem allows us to assess the risk to an individual of a known age more accurately. It achieves this by conditioning the risk relative to their age. This approach is better than assuming the individual is typical of the population as a whole. Based on Bayes law, you need to consider the prevalence of a disease in a population. Also, account for the error rate of an infectious disease test. This helps evaluate the meaning of a positive test result correctly and avoid the base-rate fallacy.
Bayesian statistics (/ˈbeɪziən/BAY-zee-ən or /ˈbeɪʒən/BAY-zhən)[1] is a theory in the field of statistics based on the Bayesian interpretation of probability, where probability expresses a degree of belief in an event. The degree of belief may be based on prior knowledge about the event, such as the results of previous experiments, or on personal beliefs about the event. This differs from a number of other interpretations of probability, such as the frequentist interpretation, which views probability as the limit of the relative frequency of an event after many trials.[2] More concretely, analysis in Bayesian methods codifies prior knowledge in the form of a prior distribution.
Automatic identification and data capture (AIDC) refers to the methods of automatically identifying objects, collecting data about them, and entering that data directly into computer systems (i.e. without human involvement). Technologies typically considered as part of AIDC include bar codes, Radio Frequency Identification (RFID), biometrics, magnetic stripes, Optical Character Recognition (OCR), smart cards, and voice recognition. AIDC is also commonly referred to as “Automatic Identification,” “Auto-ID,” and “Automatic Data Capture.” http://en.wikipedia.org/wiki/Automatic_identification_and_data_capture
Adaptive control is the control method used by a controller which must adapt to a controlled system with parameters which vary, or are initially uncertain. For example, as an aircraft flies, its mass will slowly decrease as a result of fuel consumption; a control law is needed that adapts itself to such changing conditions.
The challenges facing information today are closely related to the complexity of data management, technology and social factors. Here are some of the biggest challenges:
Data’s most significant challenges today are multifaceted and affect organizations across various industries. Here are some of the most important ones:
Data Warehousing means monolithic and siloed teams?
“Great things in business are never done by one person;
they’re done by a team of people.”
Steve Jobs
Martyn Richard Jones, Tours, 4th October 2024
Narrator: There is a widespread belief amongst the know-it-all crowd that data warehousing and business intelligence necessarily mean monolithic and siloed teams. And that the only way of moving away from such team organisations is to kill off data warehousing. But is this really a rational, coherent, and cohesive approach, as some people say it is? Or is it destructive stupidity born out of conceit, ignorance, and arrogance?
Narrator: Here, in this piece, we wander down Differential Avenue to look at what people consider domain and subject orientation. This story is about the good, the embarrassingly lousy hyperbole and then the ugliest provocative nonsense.
Martyn Richard Jones
GOOD STRATEGY 