Probabilistic Reasoning and Bayesian Networks

Probabilistic Reasoning and Bayesian Networks
Lecture Prepared For COMP Yue-Ling Wong

Probabilistic Robotics
A relatively new approach to robotics Deals with uncertainty in robot perception and action The key idea is to represent uncertainty explicitly using the calculus of probability theory i.e. represent information by probability distributions over a whole space of guesses, instead of relying on a single "best guess"

3 Parts of this Lecture Part 1. Acting Under Uncertainty
Part 2. Bayesian Networks Part 3. Probabilistic Reasoning in Robotics

Reference for Part 3 Sebastian Thrun, et. al. (2005) Probabilistic Robotics The book covers major techniques and algorithms in localization, mapping, planning and control All algorithms in the book are based on a single overarching mathematical foundations: Bayes rule its temporal extension known as Bayes filters

Goals of this lecture To introduce this overarching mathematical foundations: Bayes rule and its temporal extension known as Bayes filters To show how Bayes rule and Bayes filters are used in robotics

Preliminaries Part 1 Part 2 Probability theory Bayes rule
Bayesian Networks Dynamic Bayesian Networks

Outline of this lecture
Part 1. Acting Under Uncertainty (October 20) To go over fundamentals on probability theory that is necessary to understand the materials of Bayesian reasoning Start with AI perspective and without adding the temporal aspect of robotics Part 2. Bayesian Networks (October 22) DAG representation of random variables Dynamic Bayesian Networks (DBN) to handle uncertainty and changes over time Part 3. Probabilistic Reasoning in Robotics (October 22) To give you general ideas of how DBN is used in robotics to handle the changes of sensor and control data over time in making inferences Demonstrate use of Bayes rule and Bayes filter in a simple example of mobile robot monitoring the status (open or closed) of doors

Historical Background and Applications of Bayesian Probabilistic Reasoning
Bayesian probabilistic reasoning has been used in AI since 1960, especially in medical diagnosis One system outperformed human experts in the diagnosis of acute abdominal illness (de Dombal, et. al. British Medical Journal, 1974)

Directed Acyclic Graph (DAG) representation for Bayesian reasoning started in the 1980's Example systems using Bayesian networks (1980's-1990's): MUNIN system: diagnosis of neuromuscular disorders PATHFINDER system: pathology

NASA AutoClass for data analysis finds the set of classes that is maximally probable with respect to the data and model Bayesian techniques are utilized to calculate the probability of a call being fraudulent at AT&T

By far the most widely used Bayesian network systems: The diagnosis-and-repair modules (e.g. Printer Wizard) in Microsoft Windows (Breese and Heckerman (1996). Decision-theoretic troubleshooting: A framework for repair and experiment. In Uncertainty in Artificial Intelligence: Proceedings of the Twelfth Conference, pp ) Office Assistant in Microsoft Office (Horvitz, Breese, Heckerman, and Hovel (1998). The Lumiere project: Bayesian user modeling for inferring the goals and needs of software users. In Uncertainty in Artificial Intelligence: Proceedings of the Fourteenth Conference, pp Bayesian inference for spam filtering

An important application of temporal probability models: Speech recognition

References and Sources of Figures
Part 1: Stuart Russell and Peter Norvig, Artificial Intelligence A Modern Approach, 2nd ed., Prentice Hall, Chapter 13 Part 2: Stuart Russell and Peter Norvig, Artificial Intelligence A Modern Approach, 2nd ed., Prentice Hall, Chapters 14 & 15 Part 3: Sebastian Thrun, Wolfram Burgard, and Dieter Fox, Probabilistic Robotics, Chapter 2

Part 1 of 3: Acting Under Uncertainty

Uncertainty Arises The agent's sensors give only partial, local information about the world Existence of noise of sensor data Uncertainty in manipulators Dynamic aspects of situations (e.g. changes over time)

Degree of Belief An agent's knowledge can at best provide only a degree of belief in the relevant sentences. One of the main tools to deal with degrees of belief will be probability theory.

Probability Theory Assigns to each sentence a numerical degree of belief between 0 and 1.

In Probability Theory You may assign 0.8 to the a sentence: "The patient has a cavity." This means you believe: "The probability that the patient has a cavity is 0.8." It depends on the percepts that the agent has received to date. The percepts constitute the evidence on which probability assessments are based.

Versus In Logic You assign true or false to the same sentence. True or false depends on the interpretation and the world.

Terminology Prior or unconditional probability
The probability before the evidence is obtained. Posterior or conditional probability The probability after the evidence is obtained.

Example Suppose the agent has drawn a card from a shuffled deck of cards. Before looking at the card, the agent might assign a probability of 1/52 to its being the ace of spades. After looking at the card, the agent has obtained new evidence. The probability for the same proposition (the card being the ace of spades) would be 0 or 1.

Terminology and Basic Probability Notation

Proposition Ascertain that such-and-such is the case.

Random variable Refers to a "part" of the world whose "status" is initially unknown. Example: Cavity might refer to whether the patient's lower left wisdom tooth has a cavity. Convention used here: Capitalize the names of random variables.

Domain of a random variable The collection of values that a random variable can take on. Example: The domain of Cavity might be: true, false The domain of Weather might be: sunny, rainy, cloudy, snow

Abbreviations used here: cavity to represent Cavity = true cavity to represent Cavity = false snow to represent Weather = snow cavity  toothache to represent: Cavity=true  Toothache=false

Cavity=true  Toothache=false
Terminology and Basic Probability Notation cavity  toothache or Cavity=true  Toothache=false is a proposition that may be assigned with a degree of belief

Prior or unconditional probability The degree of belief associated with a proposition in the absence of any other information. Example: p(Cavity=true) = 0.1 or p(cavity) = 0.1

p(Weather=sunny) = 0.7 p(Weather=rain) = 0.2 p(Weather=cloudy) = 0.08
Terminology and Basic Probability Notation p(Weather=sunny) = 0.7 p(Weather=rain) = 0.2 p(Weather=cloudy) = 0.08 p(Weather=snow) = 0.02 or we may simply write P(Weather) = 0.7, 0.2, 0.08, 0.02

Prior probability distribution A vector of values for the probabilities of each individual state of a random variable Example: This denotes a prior probability distribution for the random variable Weather. P(Weather) = 0.7, 0.2, 0.08, 0.02

Joint probability distribution The probabilities of all combinations of the values of a set of random variables. P(Weather, Cavity) denotes the probabilities of all combinations of the values of a set of random variables Weather and Cavity.

can be represented by a 4x2 table of probabilities.
Terminology and Basic Probability Notation P(Weather, Cavity) can be represented by a 4x2 table of probabilities. Cavity=true Cavity=false Weather=sunny Weather=rainy Weather=cloudy Weather=snow

Full joint probability distribution The probabilities of all combinations of the values of the complete set of random variables.

Example: Suppose the world consists of just the variables Cavity, Toothache, and Weather. P(Cavity, Toothache, Weather) denotes the full joint probability distribution which can be represented as a 2x2x4 table with 16 entries.

Posterior or conditional probability Notation: p(a|b) Read as: "The probability of proposition a, given that all we know is proposition b."

Example: p(cavity|toothache) = 0.8 Read as: "If a patient is observed to have a toothache and no other information is yet available, then the probability of the patient's having a cavity will be 0.8."

Equation: where p(b) > 0

which is rewritten from the previous equation
Terminology and Basic Probability Notation Product rule which is rewritten from the previous equation

can also be written the other way around
Terminology and Basic Probability Notation Product rule can also be written the other way around

Intuition cavity  toothache cavity toothache
Terminology and Basic Probability Notation Intuition cavity  toothache cavity toothache

Derivation of Bayes' Rule

Bayes' rule, Bayes' law, or Bayes' theorem
Terminology and Basic Probability Notation Bayes' rule, Bayes' law, or Bayes' theorem

Bayesian Spam Filtering
Given that it has certain words in an , the probability that the is spam is equal to the probability of finding those certain words in spam , times the probability that any is spam, divided by the probability of finding those words in any

Speech Recognition Given the acoustic signal, the probability that the signal corresponds to the words is equal to the probability of getting the signal with the words, times the probability of finding those words in any speech, times a normalization coefficient

Conditional distribution Notation: P(X|Y) It gives the values of p(X=xi | Y=yj) for each possible i, j.

Conditional distribution Example: P(X,Y) = P(X|Y)P(Y) denotes a set of equations: p(X=x1  Y=y1)= p(X=x1 | Y=y1)p(Y=y1) p(X=x1  Y=y2)= p(X=x1 | Y=y2)p(Y=y2) . . .

Probabilistic Inference Using Full Joint Distributions

Simple dentist diagnosis example. 3 Boolean variables: Toothache
Terminology and Basic Probability Notation Simple dentist diagnosis example. 3 Boolean variables: Toothache Cavity Catch (the dentist's steel probe catches in the patient's tooth)

A full joint distribution for the Toothache, Cavity, Catch world
0.108 0.012 0.072 0.008 cavity 0.016 0.064 0.144 0.576

Getting information from the full joint distribution
toothache toothache catch catch cavity 0.108 0.012 0.072 0.008 cavity 0.016 0.064 0.144 0.576 p(cavity  toothache) = = 0.28

toothache toothache catch catch cavity 0.108 0.012 0.072 0.008 cavity 0.016 0.064 0.144 0.576 p(cavity) = = 0.2 unconditional or marginal probability

Marginalization, Summing Out, Theorem of Total Probability, and Conditioning

toothache toothache catch catch cavity 0.108 0.012 0.072 0.008 cavity 0.016 0.064 0.144 0.576 p(cavity) = = 0.2 p(cavity, catch, toothache) + p(cavity, catch, toothache) + p(cavity, catch, toothache) + p(cavity, catch, toothache)

Marginalization Rule Marginalization rule
For any sets of variables Y and Z, A distribution over Y can be obtained by summing out all the other variables from any joint distribution containing Y.

A variant of the rule after applying the product rule
Conditioning For any sets of variables Y and Z, Read as: Y is conditioned on the variable Z. Often referred to as Theorem of total probability.

toothache toothache catch catch cavity 0.108 0.012 0.072 0.008 cavity 0.016 0.064 0.144 0.576 The probability of a cavity, given evidence of a toothache: conditional probability

Independence

Independence If the propositions a and b are independent, then
p(a|b) = p(a) p(b|a) = p(b) p(ab) = p(a,b) = p(a)p(b) Think about the coin flipping example.

Independence Example Suppose Weather and Cavity are independent.
p(cavity | Weather=cloudy) = p(cavity) p(Weather=cloudy | cavity) = p(Weather=cloudy) p(cavity, Weather=cloudy) = p(cavity)p(Weather=cloudy)

Similarly… If the variables X and Y are independent, then
P(X|Y) = P(X) P(Y|X) = P(Y) P(X,Y) = P(X)P(Y)

Normalization

Previous Example toothache toothache catch catch cavity 0.108 0.012
0.072 0.008 cavity 0.016 0.064 0.144 0.576 The probability of a cavity, given evidence of a toothache:

Normalization The term
remains constant, no matter which value of Cavity we calculate. In fact, it can be viewed as a normalization constant for the distribution P(Cavity|toothache), ensuring that it adds up to 1.

Recall this example… toothache toothache catch catch cavity 0.108
0.012 0.072 0.008 cavity 0.016 0.064 0.144 0.576 The probability of a cavity, given evidence of a toothache:

Now, normalization simplifies the calculation
toothache toothache catch catch cavity 0.108 0.012 0.072 0.008 cavity 0.016 0.064 0.144 0.576 The probability distribution of Cavity, given evidence of a toothache:

Example of Probabilistic Inference: Wumpus World

Pit? Wumpus OK Pit? Wumpus

Pit? Wumpus Pit? Wumpus

Pit? Wumpus Pit? Wumpus Pit? Wumpus

Now what?? Pit? Wumpus Pit? Wumpus Pit? Wumpus

By applying Bayes' rule, you can calculate the probabilities of these cells having a pit, based on the known information. 0.31 Pit? Wumpus Pit? Wumpus 0.86 Pit? Wumpus 0.31

To Calculate the Probability Distribution for Wumpus Example

Let unknown be a composite variable consisting of Pi,j variables for squares other than Known squares and the query square [1,3]

Probabilistic Reasoning and Bayesian Networks

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