2014_0287_bayes networks.ppt
TRANSCRIPT
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1.Bayesian Networks2.Conditional Independence3.Creating Tables4.Notations for Bayesian Networks5.5.Calculating conditional probabilities Calculating conditional probabilities
from the tablefrom the tables6.6.Calculating conditional independenceCalculating conditional independence7.7.Markov Chain Monte CarloMarkov Chain Monte Carlo8.8.Markov Models.Markov Models.9.9.Markov Models and Probabilistic Markov Models and Probabilistic
methods in visionmethods in vision
Used in Spring 2012, Spring 2013, Winter 2014 (partially)
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Introduction to Introduction to Probabilistic RoboticsProbabilistic Robotics
1. Probabilities2. Bayes rule3. Bayes filters4. Bayes networks5. Markov Chains
new
next
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Bayesian Networks and Bayesian Networks and Markov ModelsMarkov Models
many many more
• Bayesian networks and Markov models :Bayesian networks and Markov models :1. Applications in User ModelingUser Modeling2. Applications in Natural Language Natural Language
ProcessingProcessing3. Applications in robotic controlrobotic control4.4. Applications in Applications in robot Visionrobot Vision
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Bayesian Networks (BNs) – Bayesian Networks (BNs) – OverviewOverview
• Introduction to BNs– Nodes, structure and probabilities– Reasoning with BNs– Understanding BNs
• Extensions of BNs– DecisionDecision Networks– DynamicDynamic Bayesian Networks (DBNs)
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Definition of Bayesian Networks
• A data structure that represents the dependence between variables
• Gives a concise specification of the joint probability joint probability distributiondistribution
• A Bayesian Network is a directed acyclic graph (DAG) in which the following holds:1. A set of random variables makes up the nodes in the
network2. A set of directed links connects pairs of nodes3. Each node has a probability distribution that quantifies
the effects of its parents
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Conditional Conditional IndependenceIndependence
The relationship between :– conditional independence – and BN structure is important for understanding how BNs work
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Conditional Independence – Causal ChainsCausal Chains
• Causal chains give rise to conditional independence
• Example: “Smoking causes cancer, which causes dyspnoea”
)|()|( BCPBACP
A B C
A B C
smoking cancer dyspnoea
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Conditional Independence – Common CausesCommon Causes
• Common Causes (or ancestors) also give rise to conditional independence
Example: Example: “Cancer is a common cause of the two symptoms: a positive Xray and dyspnoea”
BCABCPBACP | indep )|()|(
A
B
C
cancer
( ) (A indep C) | B
Xray dyspnoea
I have dyspnoea (C ) because of cancer (B) so I do not need an Xray test
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Conditional Dependence – Common Effects
• Common effects (or their descendants) give rise to conditional dependence
• Example: “Cancer is a common effect common effect of pollution and smoking”Given cancer, smoking “explains away” pollution
)| indep ()()()|( BCACPAPBCAP
A
B
C
cancer
smoking
pollution
pollutioncancer
( )
We know that you smoke and have cancer, we do not need to assume that your cancer was caused by pollution
???
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Joint Distributions Joint Distributions for describing for describing uncertain worldsuncertain worlds
• Researchers found already numerous and dramatic benefits of Joint Distributions for describing uncertain worlds
• Students in robotics and Artificial Intelligence have to understand problems with using Joint Distributions
• You should discover how Bayes Net methodology allows us to build Joint Distributions in manageable chunks
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Bayes Net methodologyBayes Net methodology
1. Bayesian Methods are one of the most important most important conceptual advances in the Machine Learning / AI field to have emerged since 1995.
2. A clean, clear, manageable language and methodology for expressing what the robot designer is certain and uncertain about
3. Already, many practical applications in medicine, factories, helpdesks: for instancefor instance1. P(this problem | these symptoms) // we will use P as probability2. anomalousness of this observation3. choosing next diagnostic test | these observations
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Why Bayesian methods matter?
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Problem 1: Problem 1: Creating Creating Joint Joint
Distribution TableDistribution Table• Joint Distribution Table is an
important concept
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Truth table of all combinations of Boolean
Variables
Probabilistic truth table • You can
guess this table, you can take data from some statistics,
• You can build this table based on some partial tables
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• Idea – use decision diagrams to represent these data.
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Use of Use of independence independence
while creating the while creating the tablestables
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Wet – Wet – Sprinkler – Sprinkler –
Rain Rain ExampleExample17
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W
S
R
Wet-Sprinkler-Rain Example
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Problem 1: Problem 1: Creating the Joint Creating the Joint
TableTable
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Our Goal is to derive this table
But the same data can be stored explicitely or implicitely, not necessarily in the form of a table!!
What extra assumptions can help to create this
table?
Let us observe that if I know 7 of these, the eight is obviously unique , as their sum = 1
So I need to guess or calculate or find 2n-1 = 7 values
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21Wet-Sprinkler-Rain Example
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22Understanding of causation
Wet-Sprinkler-Rain Example
Sprinkler on Sprinkler on under condition that it rainedrained
You need to understand causation when you create the table
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Independence simplifies probabilitiesIndependence simplifies probabilities
23Wet-Sprinkler-Rain Example
We can use these probabilities to create the
table
P(S|R) = Sprinkler on under condition that it rained
S and R are independent
We use independence of variables S and R
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This first step shows the collected data
Conditional Probability Table
(CPT)
Grass is wet
It rainedSprinkler was on
Wet-Sprinkler-Rain Example
We create the CPT for S and R based on our knowledge of the problem
What about children playing or a dog pissing?It is still possible by this value 0.1
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Full joint for only S and RFull joint for only S and R
25Wet-Sprinkler-Rain Example
Use chain rule for probabilities
0.950.900.90
0.01
Independence of S and R is used
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Chain Rule Chain Rule for Probabilitiesfor Probabilities
0.950.900.90
0.01
Random variables
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Full joint probability
Wet-Sprinkler-Rain Example
• You have a table
• You want to calculate some probability
P(~W)
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Six numbers
Wet-Sprinkler-Rain Example
We reduced only from seven to six numbers
Independence of S and R implies calculating fewer numbers Independence of S and R implies calculating fewer numbers to create the complete Joint Table for W, S and Rto create the complete Joint Table for W, S and R
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Explanation of Explanation of Diagrammatic Diagrammatic
NotationsNotationssuch as Bayes Networkssuch as Bayes Networks
You do not need to build the complete table!!
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30You can build a graph of tables or nodes which correspond to certain types of tables
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Wet-Sprinkler-Rain Example
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This first step shows the collected data
Conditional Probability Table
(CPT)
Grass is wet
It rainedSprinkler was on
Wet-Sprinkler-Rain Example
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Full joint probability
When you have this table you can modify it,
you can also calculate everything!!
• You have a table
• You want to calculate some probability
P(~W)
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Problem 2: Problem 2: Calculating conditional Calculating conditional probabilities from the probabilities from the
Joint Distribution Joint Distribution TableTable
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Probability that S=T and W=TProbability that S=T = Probability that grass is wet
under assumption that sprinkler was on
Wet-Sprinkler-Rain Example
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36Wet-Sprinkler-Rain Example
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We showed examples of both causal inference and diagnostic inference
Wet-Sprinkler-Rain Example
We will use this in next slide
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““Explaining Away” the facts Explaining Away” the facts from the tablefrom the table
38Wet-Sprinkler-Rain Example
<<
Calculated earlier from this table
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Conclusions on this problem
1. Table can be used for Explaining Away2. Table can be used to calculate
conditional independence.3. Table can be used to calculate
conditional probabilities4. Table can be used to determine
causality
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Problem 3: What if S and R Problem 3: What if S and R are dependent?are dependent?
Calculating Calculating conditional independenceconditional independence
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Conditional Independence of Conditional Independence of SS and and RR
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Wet-Sprinkler-Rain Example
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Diagrammatic notation for conditional Diagrammatic notation for conditional Independence Independence of two variablesof two variables
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Wet-Sprinkler-Rain Example
extended
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S3
S2S1
Conditional Independence formalized for Conditional Independence formalized for sets of variablessets of variables
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Now we will explain conditional independence
CLOUDY - Wet-Sprinkler-Rain Example
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Example – Example – Lung Cancer Lung Cancer
DiagnosisDiagnosis
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Example – Lung Cancer DiagnosisExample – Lung Cancer Diagnosis1. A patient has been suffering from shortness of
breath (called dyspnoea) and visits the doctor, worried that he has lung cancer.
2. The doctor knows that other diseases, such as tuberculosis and bronchitis are possible causes, as well as lung cancer.
3. She also knows that other relevant information includes whether or not the patient is a smoker (increasing the chances of cancer and bronchitis) and what sort of air pollution he has been exposed to.
4. A positive Xray would indicate either TB or lung cancer. 46
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Nodes and Values in Bayesian NetworksQ: What are the Q: What are the nodes to represent and what and what
values can they take? values can they take?A: Nodes can be discrete or continuous• Boolean nodes – represent propositions taking binary
valuesExample: Cancer node represents proposition “the patient has cancer”
• Ordered valuesExample: Pollution node with values low, medium, high
• Integral valuesExample: Age with possible values 1-120
47Lung Cancer Lung Cancer
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Lung Cancer Example: Nodes and Values
Node name Type ValuesPollution Binary {low,high}Smoker Boolean {T,F}Cancer Boolean {T,F}Dyspnoea Boolean {T,F}Xray Binary {pos,neg}
Example of variables as nodes in BN
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Lung Cancer Example: Bayesian Network StructureBayesian Network Structure
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Pollution Smoker
Cancer
Xray Dyspnoea
Lung Cancer Lung Cancer
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CConditional onditional PProbability robability TTablesables (CPTs) (CPTs)
in Bayesian Networks in Bayesian Networks
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Conditional Probability Conditional Probability TablesTables (CPTs (CPTs) in ) in Bayesian NetworksBayesian Networks
After specifying topology, we must specify specify
the CPT the CPT for each discrete node1. Each row of CPT contains the
conditional probability of each node value for each possible combination of values in its parent nodes
2. Each row of CPT must sum to 13. A CPT for a Boolean variable with n
Boolean parents contains 2n+1 probabilities
4. A node with no parents has one row (its prior probabilities)
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Lung Cancer Example: example of CPT
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Pollution low
Smoking is true
C= cancerP = pollutionS = smokingX = XrayD = Dyspnoea
Bayesian Network for cancer
Probability of cancer given state of variables P and S
Lung Cancer Lung Cancer
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• Several small CPTs are used to create larger JDTs.
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The Markov Property Markov Property for Bayesian Networks
• Modelling with BNs requires assuming the Markov Property:– There are no direct dependencies in the system
being modelled which are not already explicitly shown via arcs
• Example: smoking can influence dyspnoeadyspnoea only through causing cancer
Markov’s Idea: Markov’s Idea: all information is modelled by arcs
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Software NETICA Software NETICA for for
Bayesian Networks Bayesian Networks and and
joint probabilitiesjoint probabilities
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Reasoning with Numbers – Using Netica software
56Here are the collected dataLung Cancer Lung Cancer
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Representing the Representing the Joint Probability Joint Probability Distribution: Distribution: ExampleExample
)|Pr()|Pr(
),|Pr(
)Pr()Pr(
)Pr(
TCTDTCposX
FSlowPTC
FSlowP
TDposXTCFSlowP
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P = pollutionS = smokingX = XrayD = Dyspnoea
This graph shows how we can calculate the joint probability from other probabilities in the network
We want to calculate this
Lung Cancer Lung Cancer
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Problem 4:Problem 4:Determining Causality Determining Causality
and Bayes Nets: and Bayes Nets:
Advertisement Advertisement exampleexample
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Causality and Bayes Nets: Causality and Bayes Nets: Advertisement exampleAdvertisement example• Bayes nets allow one to learn about causal
relationships• One more Example:– Marketing analysts want to know whether to increase,
decrease or leave unchanged the exposure of some advertisement in order to maximize profit from the sale of some product
– Advertised (A) and Buy (B) will be variables for someone having seen the advertisement seen the advertisement or purchased the product
Advertised-Buy Example
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Causality ExampleExample1. So we want to know the probability that B=true given that
we force A=true, or A=false
2. We could do this by finding two similar populations and observing B based on A=trueA=true for one and A=false for the other
3. But this may be difficult or expensive difficult or expensive to find such populations
Advertised-Buy Example
–Buy (B) will be variables for someone purchased the product
–Advertised (A) seen the advertisementseen the advertisement
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How causality can be How causality can be represented in a represented in a
graph?graph?
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Markov condition and Causal Markov condition and Causal Markov ConditionMarkov Condition
• But how do we learn whether or not A causes B at all?
• The Markov Condition Markov Condition states:– Any node in a Bayes net is conditionally independent of its
non-descendants given its parents
• The CAUSAL Markov Condition CAUSAL Markov Condition (CMC) states:– Any phenomenon in a causal net is independent of its non-is independent of its non-
effects effects given its direct causes
Advertised-Buy ExampleAdvertised (A) and Buy (B)
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Acyclic Causal Graph Acyclic Causal Graph versus versus Bayes Net Bayes Net
• Thus, if we have a directed acyclic causal graph C for variables in X, then, by the Causal Markov Condition, C is also a Bayes net is also a Bayes net for the joint probability distribution of X
• The reverse is not necessarily true—a network may satisfy the Markov condition without depicting causality
Advertised-Buy Example
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Causality Example: Causality Example: when we learn that p(b|a) and p(b|a’) are not equal
• Given the Causal Markov Condition CMCCausal Markov Condition CMC, we can infer causal relationships from conditional (in)dependence relationships learned from the datalearned from the data
• Suppose we learn with high Bayesian probability with high Bayesian probability that p(b|a)p(b|a) and p(b|a’) p(b|a’) are not equalnot equal
• Given the CMC, there are four simple causal explanations for this: (more complex ones too)
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Causality Example: four causal explanations
B causes AIf you buy more, they have more money to advertise
A causes B
If they advertise more you buy more
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Causality Example: four causal explanations
1. Hidden common cause of A and B (e.g. income)
selection bias
A and B are causes for data selection 1. (a.k.a. selection bias, 2. perhaps if database didn’t record
false instances of A and B)
In rich country they advertise more and they buy more
If you increase information about Ad in database, then you increase also information about Buy in the database
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Causality Example continued
• But we still don’t know if A But we still don’t know if A causes Bcauses B
• Suppose– We learn about the Income (I)
and geographic Location (L) of the purchaser
– And we learn with high Bayesian probability the network on the right
Advertised-Buy ExampleAdvertised (A=Ad) and Buy (B)
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Causality Example - using CMC
• Given the Causal Markov Condition CMC, the ONLY causal explanation for the conditional (in)dependence relationships encoded in the Bayes net is that Ad is a cause for Buy
• That is, none of the other relationships or combinations thereof produce the probabilistic relationships encoded here
Advertised-Buy ExampleAdvertised (Ad) and Buy (B)
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Causality in Bayes Networks
• Thus, Bayes Nets allow inference of causal inference of causal relationshipsrelationships by the Causal Markov Condition (CMC)
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Problem 5:Problem 5:Determine Determine
D-separationD-separation in in Bayesian NetworksBayesian Networks
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D-separation in Bayesian NetworksD-separation in Bayesian Networks
• We will formulate a Graphical Criterion a Graphical Criterion of conditional independence
• We can determine whether a set of nodes X is independent of another set Y, given a set of evidence nodes E, via the Markov property:Markov property:– If every undirected path from a node in X to a
node in Y is d-separated by E, then X and Y are conditionally independent conditionally independent given E
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Determining D-separation (cont)
Chain
Common Common causecause
Common Common effecteffect
• A set of nodes E d-separates two sets of nodes X and Ynodes X and Y, if every undirected path from a node in X to a node in Y is blocked given E
• A path is blocked given a set of nodes E, if there is a node Z on the path for which one of three conditions holds:1. Z is in E and Z has one arrow on the path leading in and one arrow out (chain)(chain)2. Z is in E and Z has both path arrows leading out (common cause(common cause)3. Neither Z nor any descendant of Z is in E, and both path arrows lead into Z (common effectcommon effect)
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Another Another ExampleExample of of Bayesian Networks: Bayesian Networks: AlarmAlarm
• Let us draw BN from these data
Alarm Example
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Bayes Net Bayes Net Corresponding to Corresponding to Alarm-Alarm-
Burglar problemBurglar problem
Alarm Example
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Compactness, Global Semantics, Local Compactness, Global Semantics, Local Semantics and Markov BlanketSemantics and Markov Blanket
• Compactness of Bayes Net
75Alarm Example
Burglar Earthquake
John callsMary calls
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Global Semantics, Global Semantics, Local Semantics and Local Semantics and
Markov Blanket Markov Blanket for BNsfor BNs• Useful concepts 76
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Alarm Example
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Markov’s blanket are:1.Parents2.Children3.Children’s parent
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Problem 6Problem 6How to How to
systematically Build systematically Build a Bayes Network a Bayes Network
-- -- ExampleExample
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Alarm Example
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83Alarm Example
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84Alarm Example
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85
So we add arrow
Alarm Example
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86Alarm Example
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87Alarm Example
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Bayes Net for the car that does not Bayes Net for the car that does not want to startwant to start
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Such networks can be used for robot diagnostics or diagnostic of a human done by robot
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Inference in Bayes Nets and how to Inference in Bayes Nets and how to simplify itsimplify it
89
Alarm Example
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First method of simplification: First method of simplification: EnumerationEnumeration
90Alarm Example
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91Alarm Example
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Second method: Second method: Variable Variable EliminationElimination
92
Alarm Example
Variable A was eliminated
Variable E was eliminated
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93Polytrees are better
3SAT Example
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IDEA: Convert DAG to polytrees
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Clustering is used to convert non-Clustering is used to convert non-polytree BNspolytree BNs
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Not a polytree Is a polytree
Alarm Example
EXAMPLEEXAMPLE: Clustering is used to convert : Clustering is used to convert non-polytree BNsnon-polytree BNs
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• Approximate InferenceApproximate Inference
1. Direct sampling methods2. Rejection sampling3. Likelihood weighting4. Markov chain Monte Carlo
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1. Direct 1. Direct Sampling Sampling MethodsMethods
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Direct SamplingDirect SamplingDirect Sampling generates minterms with their probabilities
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100Wet Sprinkler Rain Example
W=wetC= cloudyR = rainS = sprinkler
We start from top
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101Wet Sprinkler Rain Example
W=wetC= cloudyR = rainS = sprinkler
Cloudy = yes
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102Wet Sprinkler Rain Example
W=wetC= cloudyR = rainS = sprinkler
Cloudy = yes
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103Wet Sprinkler Rain Example
W=wetC= cloudyR = rainS = sprinkler
sprinkler = no
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104Wet Sprinkler Rain Example
W=wetC= cloudyR = rainS = sprinkler
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105Wet Sprinkler Rain Example
W=wetC= cloudyR = rainS = sprinkler
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106Wet Sprinkler Rain Example
W=wetC= cloudyR = rainS = sprinkler
We generated a sample minterm C S’ R W
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2. Rejection 2. Rejection Sampling Sampling MethodsMethods
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Rejection Sampling• Reject inconsistent samples
108Wet Sprinkler Rain Example
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3. Likelihood 3. Likelihood weighting weighting methodsmethods
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111Wet Sprinkler Rain Example
W=wetC= cloudyR = rainS = sprinkler
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112Wet Sprinkler Rain Example
W=wetC= cloudyR = rainS = sprinkler
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113Wet Sprinkler Rain Example
W=wetC= cloudyR = rainS = sprinkler
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114Wet Sprinkler Rain Example
W=wetC= cloudyR = rainS = sprinkler
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115Wet Sprinkler Rain Example
W=wetC= cloudyR = rainS = sprinkler
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116Wet Sprinkler Rain Example
W=wetC= cloudyR = rainS = sprinkler
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117Wet Sprinkler Rain Example
W=wetC= cloudyR = rainS = sprinkler
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Likelihood weighting Likelihood weighting vs. rejection rejection samplingsampling
1. Both generate consistent estimates of the joint distribution conditioned on the values of the evidence variables
2. Likelihood weighting converges faster converges faster to the correct probabilities
3. But even likelihood weighting degrades likelihood weighting degrades with many evidence variables because a few samples will have nearly all the total weight
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SourcesProf. David PageMatthew G. Lee
Nuria Oliver,Barbara Rosario
Alex PentlandEhrlich Av
Ronald J. WilliamsAndrew Moore tutorial with the same title
Russell &Norvig’s AIMA site Alpaydin’s Introduction to Machine Learning site.