// ~->[DNET-1]->~

// File created by Norsys using Netica 3.12 on Apr 13, 2006 at 19:17:05.

bnet ChestClinic {
AutoCompile = TRUE;
autoupdate = TRUE;
comment = "\n\
	Chest Clinic                                       Text Copyright 1998 Norsys Software Corp.\n\n\
	This Bayes net is also known as \"Asia\", and is an example which is popular \n\
	for introducing Bayes nets.  It is from Lauritzen&Spiegelhalter88 (see below).\n\
	It is for example purposes only, and should not be used for real decision making.\n\n\
	It is a simplified version of a network that could be used to diagnose patients arriving\n\
	at a clinic.  Each node in the network corresponds to some condition of the patient,\n\
	for example, \"Visit to Asia\" indicates whether the patient recently visited Asia.\n\
	To diagnose a patient, values are entered for nodes when they are known.  \n\
	Netica then automatically re-calculates the probabilities for all the other nodes,\n\
	based on the relationships between them.  The links between the nodes indicate how the\n\
	relationships between the nodes are structured.\n\n\
	The two top nodes are for predispositions which influence the likelihood of the diseases. \n\
	Those diseases appear in the row below them.  At the bottom are symptoms of the diseases.\n\
	To a large degree, the links of the network correspond to causation.  \n\
	This is a common structure for diagnostic networks: predisposition nodes at the top, \n\
	with links to nodes representing internal conditions and failure states, which in turn have\n\
	links to nodes for observables.  Often there are many layers of nodes representing\n\
	internal conditions, with links between them representing their complex inter-relationships.\n\n\
	This network is from Lauritzen, Steffen L. and David J. Spiegelhalter (1988) \"Local \n\
	computations with probabilities on graphical structures and their application to expert \n\
	systems\" in Journal Royal Statistics Society B, 50(2), 157-194.\n\n\n\
	TUTORIAL:            Basic Probabilistic Inference\n\
	--------\n\n\
	Keep in mind when doing tutorials that there is a great deal of assitance available\n\
	from Netica's onscreen help, often about the exact networks of the tutorials.\n\
	For this example, choose Help->Contents/Index, click on the Index tab, type in\n\
	\"Asia\", and go to the example.\n\n\
	All the information contained in a Bayes net can be observed by examining 3 things.\n\n\
	First, there is the network structure, consisting of the nodes and their links,\n\
	which you can see in the network diagram currently being displayed.\n\n\
	Second, are the properties of each node, which you can see in their node dialog box,\n\
	obtained by double-clicking on the node.\n\n\
	Third, are the actual relationships between the nodes, which you can see by \n\
	single-clicking on a node to select it, then choosing Relation->View/Edit.  \n\
	The relationship may be probabilistic or functional.  For example, click on \n\
	\"Lung Cancer\", and then choose Relation->View/Edit, to see its probabilistic relation \n\
	with Smoking (the numbers are for example purposes only, and may not reflect reality).\n\
	If you click on \"Tuberculosis or Cancer\", and choose Relation->View/Edit, you can see\n\
	its functional dependence on Tuberculosis and Lung Cancer.\n\n\
	To compile the network for use, click on its window to make it active,\n\
	and choose Network->Compile. \n\n\
	The appropriate data structures for fast inference have been built internally.  \n\
	The bars in each node have darkened, indicating that they and the numbers beside them\n\
	are now valid data.  The indicate the probabilities of each state of the node.\n\n\
	Suppose we want to \"diagnose\" a new patient.  When she first enters the clinic,\n\
	without having any information about her, we believe she has lung cancer with a\n\
	probability of 5.5%, as can be seen on the Lung Cancer node (the number may be higher\n\
	than that for the general population, because something has led her to the chest clinic).\n\n\
	If she has an abnormal x-ray, that information can be entered by clicking on the word\n\
	\"Abnormal\" of the \"XRay Result\" node (in a real-world Bayes net, you would probably\n\
	be able to enter in exactly what way the x-ray was \"abnormal\").\n\n\
	All the probability numbers and bars will change to take into account the finding.\n\
	Now the probability that she has lung cancer has increased to 48.9%.\n\n\
	If you further indicate that she has made a visit to asia recently, by clicking on\n\
	\"Visit\", the probability of lung cancer decreases to 37.1%, because the abnormal XRay is \n\
	partially explained away by a greater chance of Tuberculosis (which she could \n\
	catch in Asia).  Old fashioned medical expert systems had problems with this kind of \n\
	reasoning, since each of the findings \"Abnormal XRay\" and \"Visit to Asia\" by themselves\n\
	increase or leave the same the probability of lung cancer.\n\n\
	You can try entering and changing some more findings.  To remove a finding, simply click\n\
	on its name again.  If you want to remove all the findings (a new patient has just walked\n\
	in), choose Network->Remove Findings.\n\n\n\n\n\
	";
whenchanged = 1144980864;

visual V1 {
	defdispform = BELIEFBARS;
	nodelabeling = TITLE;
	NodeMaxNumEntries = 50;
	nodefont = font {shape= "Arial"; size= 14;};
	linkfont = font {shape= "Arial"; size= 9;};
	windowposn = (7, 6, 816, 498);
	CommentShowing = TRUE;
	CommentWindowPosn = (6, 499, 817, 707);
	resolution = 72;
	drawingbounds = (1104, 730);
	showpagebreaks = FALSE;
	usegrid = TRUE;
	gridspace = (6, 6);
	NodeSet Node {BuiltIn = 1; Color = 0xc0c0c0;};
	NodeSet Nature {BuiltIn = 1; Color = 0xf8eed2;};
	NodeSet Deterministic {BuiltIn = 1; Color = 0xd3caa6;};
	NodeSet Finding {BuiltIn = 1; Color = 0xa0a0a0;};
	NodeSet Constant {BuiltIn = 1; Color = 0xffffff;};
	NodeSet ConstantValue {BuiltIn = 1; Color = 0xffffb4;};
	NodeSet Utility {BuiltIn = 1; Color = 0xffbdbd;};
	NodeSet Decision {BuiltIn = 1; Color = 0xdee8ff;};
	NodeSet Documentation {BuiltIn = 1; Color = 0xf0fafa;};
	NodeSet Title {BuiltIn = 1; Color = 0xffffff;};
	NodeSet node {Color = 0xffffff;};
	NodeSet nature {Color = 0xffffff;};
	NodeSet utility {Color = 0xffffff;};
	NodeSet decision {Color = 0xffffff;};
	NodeSet finding {Color = 0xffffff;};
	NodeSet constant {Color = 0xffffff;};
	NodeSet constant_set {Color = 0xffffff;};
	PrinterSetting A {
		margins = (1270, 1270, 1270, 1270);
		landscape = FALSE;
		magnify = 1;
		};
	};

node VisitAsia {
	kind = NATURE;
	discrete = TRUE;
	chance = CHANCE;
	states = (visit, no_visit);
	parents = ();
	probs = 
		// visit        no visit     
		  (0.01,        0.99);
	title = "Visit To Asia";
	comment = "Patient has recently visited Asia";
	whenchanged = 1080240172;
	belief = (0.01, 0.99);
	visual V1 {
		center = (126, 54);
		height = 7;
		};
	};

node Tuberculosis {
	kind = NATURE;
	discrete = TRUE;
	chance = CHANCE;
	states = (present, absent);
	parents = (VisitAsia);
	probs = 
		// present      absent         // VisitAsia 
		 ((0.05,        0.95),         // visit     
		  (0.01,        0.99));        // no visit  ;
	title = "Tuberculosis";
	whenchanged = 1084319357;
	belief = (0.0104, 0.9896);
	visual V1 {
		center = (126, 156);
		height = 1;
		};
	};

node Smoking {
	kind = NATURE;
	discrete = TRUE;
	chance = CHANCE;
	states = (smoker, non_smoker);
	parents = ();
	probs = 
		// smoker       non smoker   
		  (0.5,         0.5);
	title = "Smoking";
	whenchanged = 1080240139;
	belief = (0.5, 0.5);
	visual V1 {
		center = (510, 54);
		height = 8;
		};
	};

node Cancer {
	kind = NATURE;
	discrete = TRUE;
	chance = CHANCE;
	states = (present, absent);
	parents = (Smoking);
	probs = 
		// present      absent         // Smoking    
		 ((0.1,         0.9),          // smoker     
		  (0.01,        0.99));        // non smoker ;
	title = "Lung Cancer";
	whenchanged = 1080240204;
	belief = (0.055, 0.945);
	visual V1 {
		center = (384, 156);
		height = 4;
		};
	};

node TbOrCa {
	kind = NATURE;
	discrete = TRUE;
	chance = DETERMIN;
	states = (true, false);
	parents = (Tuberculosis, Cancer);
	functable = 
		                     // Tuberculosis Cancer  
		 ((true,             // present      present 
		   true),            // present      absent  
		  (true,             // absent       present 
		   false));          // absent       absent  ;
	title = "Tuberculosis\nor Cancer";
	whenchanged = 1080240233;
	belief = (0.064828, 0.935172);
	visual V1 {
		center = (264, 264);
		height = 3;
		};
	};

node XRay {
	kind = NATURE;
	discrete = TRUE;
	chance = CHANCE;
	states = (abnormal, normal);
	parents = (TbOrCa);
	probs = 
		// abnormal     normal         // TbOrCa 
		 ((0.98,        0.02),         // true   
		  (0.05,        0.95));        // false  ;
	title = "XRay Result";
	whenchanged = 1080240246;
	belief = (0.11029, 0.88971);
	visual V1 {
		center = (138, 366);
		height = 2;
		};
	};

node Bronchitis {
	kind = NATURE;
	discrete = TRUE;
	chance = CHANCE;
	states = (present, absent);
	parents = (Smoking);
	probs = 
		// present      absent         // Smoking    
		 ((0.6,         0.4),          // smoker     
		  (0.3,         0.7));         // non smoker ;
	title = "Bronchitis";
	whenchanged = 1080240216;
	belief = (0.45, 0.55);
	visual V1 {
		center = (636, 156);
		height = 6;
		};
	};

node Dyspnea {
	kind = NATURE;
	discrete = TRUE;
	chance = CHANCE;
	states = (present, absent);
	parents = (TbOrCa, Bronchitis);
	probs = 
		// present      absent          // TbOrCa Bronchitis 
		(((0.9,         0.1),           // true   present    
		  (0.7,         0.3)),          // true   absent     
		 ((0.8,         0.2),           // false  present    
		  (0.1,         0.9)));         // false  absent     ;
	title = "Dyspnea";
	comment = "Shortness of breath.";
	whenchanged = 1080240263;
	belief = (0.435971, 0.564029);
	visual V1 {
		center = (426, 366);
		height = 5;
		link 1 {
			path = ((321, 301), (368, 330));
			};
		};
	};

node TITLE1 {
	kind = ASSUME;
	discrete = FALSE;
	parents = ();
	title = "Chest Clinic";
	whenchanged = 904468693;
	visual V1 {
		center = (666, 300);
		font = font {shape= "Times New Roman"; size= 24;};
		height = 9;
		};
	};

node TITLE2 {
	kind = ASSUME;
	discrete = FALSE;
	parents = ();
	title = "Distributed by Norsys Software Corp. ";
	whenchanged = 1084319978;
	visual V1 {
		center = (672, 330);
		font = font {shape= "Times New Roman"; size= 9;};
		height = 10;
		};
	};
ElimOrder = (VisitAsia, XRay, Tuberculosis, Smoking, Cancer, TbOrCa, Bronchitis, Dyspnea);
};