Codabar for VB.NET Belief Networks in Visual Studio .NET Generator PDF417 in Visual Studio .NET Belief Networks

6.3. Belief Networks Using Barcode encoder for VS .NET Control to generate, create USS Codabar image in .NET applications.VS .NET NW-7 for VB.NET Note that the probabilit ABC Codabar for VB.NET y of tampering is not affected by observing smoke; however, the probabilities of report and re are increased. Suppose that both report and smoke were observed: P(tampering.

qrcode report smoke) = 0.0284 P( re report smoke) = 0.964 Visual Studio .NET Codabar Observing both makes re even more likely.

However, in the context of the report, the presence of smoke makes tampering less likely. This is because the report is explained away by re, which is now more likely. Suppose instead that report, but not smoke, was observed: P(tampering.

report smoke) = 0.501 P( re report smoke) = 0.029 Code 2 of 7 for VB.NET 4 In the context of the report, re becomes much less likely and so the probability of tampering increases to explain the report.

This example illustrates how the belief net independence assumption gives commonsense conclusions and also demonstrates how explaining away is a consequence of the independence assumption of a belief network. This network can be used in a number of ways:. By conditioning on the knowledge that the switches and circuit breakers are ok, and on the values of the outside power and the position of the switches, this network can simulate how the lighting should work. Given values of the outside power and the position of the switches, the network can infer the likelihood of any outcome for example, how likely it is that l1 is lit. Given values for the switches and whether the lights are lit, the posterior probability that each switch or circuit breaker is in any particular state can be inferred.

Given some observations, the network can be used to reason backward to determine the most likely position of switches. Given some switch positions, some outputs, and some intermediate values, the network can be used to determine the probability of any other variable in the network..

6.3.1 Constructing Belief Networks To represent a domain in a belief network, the designer of a network must consider the following questions:. What are the relevant variables In particular, the designer must consider what the agent may obs erve in the domain. Each feature that can be observed should be a variable, because the agent must be able to condition on all of its observations. what information the agent is interested in knowing the probability of, given the observations.

Each of these features should be made into a variable that can be queried.. 6. Reasoning Under Uncertainty other hidden variables or latent variables that will not be observed or queried but that make the model simpler. These variables either account for dependencies or reduce the size of the speci cation of the conditional probabilities..

What values should the .NET Uniform Symbology Specification Codabar se variables take This involves considering the level of detail at which the agent should reason to answer the sorts of queries that will be encountered. For each variable, the designer should specify what it means to take each value in its domain.

What must be true in the world for a variable to have a particular value should satisfy the clarity principle (page 114). It is a good idea to explicitly document the meaning of all variables and their possible values. The only time the designer may not want to do this is when a hidden variable exists whose values the agent will want to learn from data [see Section 11.

2.2 (page 460)]. What is the relationship between the variables This should be expressed in terms of local in uence and be modeled using the parent relation.

How does the distribution of a variable depend on the variables that locally in uence it (its parents) This is expressed in terms of the conditional probability distributions.. Example 6.14 Suppose you want the diagnostic assistant to be able to reason about the possible causes of a patient s wheezing and coughing, as in Example 5.30 (page 201).

. The agent can observe Uniform Symbology Specification Codabar for Visual Basic .NET coughing, wheezing, and fever and can ask whether the patient smokes. There are thus variables for these.

The agent may want to know about other symptoms of the patient and the prognosis of various possible treatments; if so, these should also be variables. (Although they are not used in this example). There are variables that are useful to predict the outcomes of patients.

The medical community has named many of these and characterized their symptoms. Here we will use the variables Bronchitis and In uenza. Now consider what the variables directly depend on.

Whether patients wheeze depends on whether they have bronchitis. Whether they cough depends on on whether they have bronchitis. Whether patients have bronchitis depends on whether they have in uenza and whether they smoke.

Whether they have fever depends on whether they have in uenza. Figure 6.3 depicts these dependencies.

Choosing the values for the variables involves considering the level of detail at which to reason. You could encode the severity of each of the diseases and symptoms as values for the variables. You could, for example, use the values severe, moderate, mild, or absent for the Wheezing variable.

You could even model the disease at a lower level of abstraction, for example, by representing all subtypes of the diseases. For ease of exposition, we will model the domain at a very abstract level, only considering the presence or absence of symptoms and diseases. Each of the variables will be.

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