L18
... certainty reasoning methods. ● Also they have well-defined semantics for decision making. Disadvantages: ● They require a significant amount of probability data to construct a KB. − For example, a diagnostic system having 50 detectable conclusions (R) and 300 relevant and observable characteristics ...
... certainty reasoning methods. ● Also they have well-defined semantics for decision making. Disadvantages: ● They require a significant amount of probability data to construct a KB. − For example, a diagnostic system having 50 detectable conclusions (R) and 300 relevant and observable characteristics ...
Lecture Notes for Week 5
... follows a regular pattern, assuming there is no relationship between the row and column variables. It is also somewhat bell-shaped like z, but unlike z it has a longer right tail (is right-skewed) and by construction only takes positive values (its curve is entirely to the right of zero instead of b ...
... follows a regular pattern, assuming there is no relationship between the row and column variables. It is also somewhat bell-shaped like z, but unlike z it has a longer right tail (is right-skewed) and by construction only takes positive values (its curve is entirely to the right of zero instead of b ...
View document - The Open University
... In this area many different kinds of samples are drawn for many different purposes. They include surveys to explore the demographic characteristics of a set of people, that is, their social environment, economic functioning, health, opinions and activities. Many such surveys are conducted for govern ...
... In this area many different kinds of samples are drawn for many different purposes. They include surveys to explore the demographic characteristics of a set of people, that is, their social environment, economic functioning, health, opinions and activities. Many such surveys are conducted for govern ...
Seciton 7-1 - s3.amazonaws.com
... studying a foreign language. What is the probability that a randomly selected student is not studying a foreign language? ...
... studying a foreign language. What is the probability that a randomly selected student is not studying a foreign language? ...
Killer Whales
... feeding grounds and meet in other habitats. Mating has rarely been observed, but many workers believe it occurs when pods encounter each other. It is not known how new pods form. Patterns of similarities in dialects between pods suggest that some were probably more closely linked in the past, and ma ...
... feeding grounds and meet in other habitats. Mating has rarely been observed, but many workers believe it occurs when pods encounter each other. It is not known how new pods form. Patterns of similarities in dialects between pods suggest that some were probably more closely linked in the past, and ma ...
mex09sug_gs
... It is basically an estimation of the probability that a single or joint event occurs. We could define the event in terms of the levels of one or more variables, for one or more future time periods. ...
... It is basically an estimation of the probability that a single or joint event occurs. We could define the event in terms of the levels of one or more variables, for one or more future time periods. ...
A new look at inference for the Hypergeometric
... derivations and allows us to test intuitive understanding of Bayesian reasoning. A somewhat surprising feature of the posterior predictive distribution of the number of successes in a future sample is that it is independent of the size of the population. The explanation of this phenomenon by Bose an ...
... derivations and allows us to test intuitive understanding of Bayesian reasoning. A somewhat surprising feature of the posterior predictive distribution of the number of successes in a future sample is that it is independent of the size of the population. The explanation of this phenomenon by Bose an ...
Dependent and Independent Events
... . . . if the drawn card is red? P(J♠ if R) = 0 Note that in each scenario, we are given additional information about the card that reduces the size of our sample space S. ...
... . . . if the drawn card is red? P(J♠ if R) = 0 Note that in each scenario, we are given additional information about the card that reduces the size of our sample space S. ...
More on 2 × 2 Tables
... • Poisson Sampling: A sample from a single population is sampled and each member falls into one of the four cells of the 2 × 2 table. An example is the infant deaths in New York. Each child born was classified by birth weight (≤ 2500g vs > 2500g) and mortality status at one year. In this scheme non ...
... • Poisson Sampling: A sample from a single population is sampled and each member falls into one of the four cells of the 2 × 2 table. An example is the infant deaths in New York. Each child born was classified by birth weight (≤ 2500g vs > 2500g) and mortality status at one year. In this scheme non ...
Event
... • Favorable outcomes – The number of ways within the sample space that the event (what you want to occur) CAN occur • Total number of outcomes – everything that can happen in the sample space • In these two counts you are counting the options, the outcomes are not the options – it is the number of o ...
... • Favorable outcomes – The number of ways within the sample space that the event (what you want to occur) CAN occur • Total number of outcomes – everything that can happen in the sample space • In these two counts you are counting the options, the outcomes are not the options – it is the number of o ...
Statistics
Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data. In applying statistics to, e.g., a scientific, industrial, or societal problem, it is conventional to begin with a statistical population or a statistical model process to be studied. Populations can be diverse topics such as ""all persons living in a country"" or ""every atom composing a crystal"". Statistics deals with all aspects of data including the planning of data collection in terms of the design of surveys and experiments.When census data cannot be collected, statisticians collect data by developing specific experiment designs and survey samples. Representative sampling assures that inferences and conclusions can safely extend from the sample to the population as a whole. An experimental study involves taking measurements of the system under study, manipulating the system, and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements. In contrast, an observational study does not involve experimental manipulation.Two main statistical methodologies are used in data analysis: descriptive statistics, which summarizes data from a sample using indexes such as the mean or standard deviation, and inferential statistics, which draws conclusions from data that are subject to random variation (e.g., observational errors, sampling variation). Descriptive statistics are most often concerned with two sets of properties of a distribution (sample or population): central tendency (or location) seeks to characterize the distribution's central or typical value, while dispersion (or variability) characterizes the extent to which members of the distribution depart from its center and each other. Inferences on mathematical statistics are made under the framework of probability theory, which deals with the analysis of random phenomena.A standard statistical procedure involves the test of the relationship between two statistical data sets, or a data set and a synthetic data drawn from idealized model. An hypothesis is proposed for the statistical relationship between the two data sets, and this is compared as an alternative to an idealized null hypothesis of no relationship between two data sets. Rejecting or disproving the null hypothesis is done using statistical tests that quantify the sense in which the null can be proven false, given the data that are used in the test. Working from a null hypothesis, two basic forms of error are recognized: Type I errors (null hypothesis is falsely rejected giving a ""false positive"") and Type II errors (null hypothesis fails to be rejected and an actual difference between populations is missed giving a ""false negative""). Multiple problems have come to be associated with this framework: ranging from obtaining a sufficient sample size to specifying an adequate null hypothesis.Measurement processes that generate statistical data are also subject to error. Many of these errors are classified as random (noise) or systematic (bias), but other important types of errors (e.g., blunder, such as when an analyst reports incorrect units) can also be important. The presence of missing data and/or censoring may result in biased estimates and specific techniques have been developed to address these problems.Statistics can be said to have begun in ancient civilization, going back at least to the 5th century BC, but it was not until the 18th century that it started to draw more heavily from calculus and probability theory. Statistics continues to be an area of active research, for example on the problem of how to analyze Big data.