Pierre Bessière LPPA – Collège de France - CNRS Cours « Cognition bayésienne » 2010 The most...

Pierre Bessière — LPPA – Collège de France - CNRSCours « Cognition bayésienne » — 2010

The most incomprehensible thing about the world is that it is comprehensible

Albert Einstein

Bayesian Cognition

Julien DiardCNRS - Laboratoire de Psychologie et NeuroCognition

Pierre BessièreCNRS - Laboratoire de Physiologie de la Perception et de l’Action

Probabilistic models of action, perception, inference,

decision and learning

To get more info

Bayesian-Programming.org

ftp://ftp-serv.inrialpes.fr/pub/emotion/bayesian-programming/Cours

Pierre.Bessiere@college-de-france.fr

http://diard.wordpress.com Julien.Diard@upmf-grenoble.fr

Plan / planning

• Bessière c1 15/11 – Incomplétude, incertitude, Programme Bayésien,

inférence Bayésienne

• Diard c2 29/11, c3 13/12, c4 03/01– Modèles Bayésiens en robotique et sciences cognitives

• Diard c5 10/01 – Sélection de modèles, machine learning,

distinguabilité de modèles

• Bessière c6 17/01– Compléments : algorithmes d’inférence, maximum

d’entropie

Perception test

Daniel J. Simons & Christopher ChabrisHarvard University

Pierre Bessière — LPPA – Collège de France - CNRSCours « Cognition bayésienne » — 2010 6

http://www.youtube.com/watch?v=ubNF9QNEQLA

http://nivea.psycho.univ-paris5.fr/demos/BONETO.MOV

http://viscog.beckman.illinois.edu/flashmovie/12.php

Probability Theory

as an alternative

to LogicThe actual science of logic is conversant at present only with things either certain, impossible, or entirely doubtful, none of which (fortunately) we have to reason on. Therefore the true logic for this world is the calculus of Probabilities, which takes account of the magnitude of the probability which is, or ought to be, in a reasonable man's mind .

James Clerk Maxwell

Incompleteness

and Uncertainty

A very small cause which escapes our notice determines a considerableeffect that we cannot fail to see, and then we say that the effect is due tochance.

H. Poincaré

Shape from Image

Shape from Motion

Colas, F., Droulez, J., Wexler, M. & Bessiere, P. (2008) Unified probabilistic model of perception of three-dimensional structure from optic flow; in Biological Cybernetics,in pressColas, F. (2006) Perception des objets en mouvement : Composition bayésienne du flux optique et du mouvement de l’observateur, Thèse INPG

Credit card fraud detection

Beam-in-the-Bin experiment (Set-up)

Beam-in-the-Bin experiment (Results)

Logical Paradigm

O1begin......end

AvoidObs(01)

AIncompleteness

Bayesian Paradigm

Avoid Obstacle

ConnaissancesPréalables

R ( S , M)

SDonnées Expérimentales

=P(M | SDC)

P(MS | DC)

PrincipleIncompleteness

Uncertainty

Preliminary Knowledge+

Experimental Data=

Probabilistic Representation

Decision

Bayesian Inference

Bayesian Learning

P(a)+ P(¬ a) =1

P a∧b( ) = P a( )P b | a( ) = P b( )P a | b( )

ThesisProbabilistic inference and learning theory, considered as a

model of reasoning, is a new paradigm (an alternative to logic) to explain and understand perception, inference, decision, learning and

action.La théorie des probabilités n'est rien d'autre que le sens commun fait calcul.Marquis Pierre-Simon de Laplace

The actual science of logic is conversant at present only with things either certain, impossible, or entirely doubtful, none of which (fortunately) we have to reason on. Therefore the true logic for this world is the calculus of Probabilities, which takes account of the magnitude of the probability which is, or ought to be, in a reasonable man's mind .James Clerk Maxwell By inference we mean simply: deductive reasoning whenever enough information is at hand to permit it; inductive or probabilistic reasoning when - as is almost invariably the case in real problems - all the necessary information is not available. Thus the topic of « Probability as Logic » is the optimal processing of uncertain and incomplete knowledge .E.T. Jaynes

Subjectivist vs Objectivist epistemology of probabilities ?

A water treatment unit (1)

I0 = 2[ ]∧ I1 = 8[ ]∧ C = 0[ ]∧ H = 0[ ]

Q = IntI0 + I1 + F

⎛ ⎝ ⎜

⎞ ⎠ ⎟

O* = IntI0 + I1 +10

⎛ ⎝ ⎜

⎞ ⎠ ⎟

I0 = 2[ ]∧ I1 = 8[ ]∧ F = 8[ ]∧ H = 0[ ]

α =IntI0 + I1 + F +C − H

⎛ ⎝ ⎜

⎞ ⎠ ⎟

α , if 0 ≤ α ≤ O*( )

2O* −α( ), if α > O*( )

0, otherwise

⎪ ⎪

I0 = 2[ ]∧ I1 = 8[ ]∧ F = 8[ ]∧ C = 0[ ]

α =IntI0 + I1 + F +C − H

⎛ ⎝ ⎜

⎞ ⎠ ⎟

α , if 0 ≤ α ≤ O*( )

2O* −α( ), if α > O*( )

0, otherwise

⎪ ⎪

S = IntI0 + F

⎛ ⎝ ⎜

⎞ ⎠ ⎟

Uncertainty on O due to inaccuracy on S

I0 = 2[ ]∧ I1 = 8[ ]∧ C = 2[ ]∧ H = 0[ ]

Uncertainty due to the hidden variable H

I0 = 2[ ]∧ I1 = 8[ ]∧ C = 2[ ]

Not taking into account the effect of hidden

variables may lead to wrong decision (1)

I0 = 2[ ]∧ I1 = 8[ ]∧ F = 8[ ]

Not taking into account the effect of hidden

variables may lead to wrong decision (2)

I0 = 2[ ]∧ I1 = 8[ ]∧ F = 8[ ]

PrincipleIncompleteness

Uncertainty

Preliminary Knowledge+

Experimental Data=

Probabilistic Representation

Decision

Bayesian Inference

Bayesian Learning

P(a)+ P(¬ a) =1

P a∧b( ) = P a( )P b | a( ) = P b( )P a | b( )

Basic Concepts

Far better an approximate answer to the right question which is often

vague, than an exact answer to the wrong question which can always be

made precise.

John W. Tuckey

Bayesian Spam Detection

• Classify texts in 2 categories “spam” or “nonspam”– Only available information: a set of

• Adapt to the user and learn from experience

Variable

W0 , W1, ..., WN-1

Probability

P Spam = false[ ]( ) = 0.25

P Spam = true[ ]( ) = 0.75

Normalization postulate

P Spam = false[ ]( ) + P Spam = true[ ]( ) =1.0

P X = x[ ]( )x∈X

∑ =1.0

P X( )X

∑ =1.0

Conditional probability

P Wn = false[ ] | Spam = true[ ]( ) = 0.9996

P Wn = true[ ] | Spam = true[ ]( ) = 0.0004

P X |Y( )X

∑ =1.0

Variable conjunction

P Spam∧Wn( )

P Spam∧W0 ∧ ... ∧Wn ∧ ... ∧WN−1( )

Conjunction postulate

P X∧Y( ) = P X( ) × P Y | X( )

= P Y( ) × P X |Y( )

P X |Y( ) =P X( ) × P Y | X( )

P Y( )

P Spam = true[ ]∧ Wn = true[ ]( ) = P Spam = true[ ]( ) × P Wn = true[ ] | Spam = true[ ]( )

= 0.75 × 0.0004

= 0.0003

= P Wn = true[ ]( ) × P Spam = true[ ] | Wn = true[ ]( )

Syllogisms

• Logical Syllogisms:– Modus Ponens:– Modus Tollens:

• Probabilistic Syllogisms:– Modus Ponens:– Modus Tollens:

a∧ a ⇒ b[ ] a b

¬b∧ a ⇒ b[ ] a ¬ a

P b | a( ) =1

P b | a( ) =1 ⇔ P ¬ a |¬ b( ) =1

P b | a( ) =1⇒ P a | b( ) ≥ P a( )

P b | a( ) =1⇒ P b |¬ a( ) ≤ P b( )

a ≡"x divisible by 9"

b ≡"x divisible by 3"

Marginalization rule

P X∧Y( )X

∑ = P Y( )

Joint distribution and questions (1)

P Y( ) = P X∧Y( )X

P X( ) = P X∧Y( )Y

P Y | X( ) =P X∧Y( )

P X∧Y( )Y

P X |Y( ) =P X∧Y( )

P X∧Y( )X

P Spam∧W0 ∧ ... ∧Wn ∧ ... ∧WN−1( )

P Spam( ) = P Spam∧W0 ∧ ... ∧Wn ∧ ... ∧WN−1( )W0∧ ... ∧WN-1

P Wn( ) = P Spam∧W0 ∧ ... ∧Wn ∧ ... ∧WN−1( )Spam∧Wi≠n

P Wn | Spam = true[ ]( ) =

P Spam∧W0 ∧ ... ∧Wn ∧ ... ∧WN−1( )Wi≠n

P Spam∧W0 ∧ ... ∧Wn ∧ ... ∧WN−1( )W0∧ ... ∧WN-1

P Spam |W0 ∧ ... ∧WN-1( ) =P Spam∧W0 ∧ ... ∧Wn ∧ ... ∧WN−1( )

P Spam∧W0 ∧ ... ∧Wn ∧ ... ∧WN−1( )Spam

Decomposition

P Spam∧W0 ∧ ... ∧WN−1( )

= P Spam( ) × P W0 | Spam( ) × P W1 |W0 ∧Spam( )

× ... × P WN−1 |WN−2 ∧ ... ∧W0 ∧Spam( )

P Spam∧W0 ∧ ... ∧WN−1( )

= P Spam( ) × P Wn | Spam( )n=0

Bayesian Network

Parametric Forms (1)

P Spam = false[ ]( ) = 0.25

P Wn | Spam = false[ ]( ) =Nbre f

Nbre f

P Wn | Spam = true[ ]( ) =Nbret

P Spam∧W0 ∧ ... ∧WN−1( )

Parametric Forms (2)

P Spam∧W0 ∧ ... ∧WN−1( )

P Spam = false[ ]( ) = 0.25

P Wn | Spam = false[ ]( ) =1+ Nbre f

2 + Nbre f

P Wn | Spam = true[ ]( ) =1+ Nbret

2 + Nbret

Identification

2 + Nbre f

2 + Nbret

Specification = Variables + Decomposition

+ Parametric Forms

• Variables: the choice of relevant variables for the problem

• Decomposition: the expression of the joint probability distribution as the product of simpler distribution

• Parametric Forms: the choice of the mathematical functions of each of these distributions

Description = Specification + Identification

2 + Nbre f

2 + Nbret

Questions (1)

P Spam∧W0 ∧ ... ∧Wn ∧ ... ∧WN−1( ) = P Spam( ) × P Wn | Spam( )n=0

P Spam( ) = P Spam( ) × P Wn | Spam( )n=0

∏W0∧ ... ∧WN-1

= P Spam( )

Questions (2)

P Wn( ) = P Spam( ) × P Wn | Spam( )n=0

∏Spam∧Wi≠n

= P Spam( ) × P Wn | Spam( )Spam

P Wn = true[ ]( ) = 0.25 ×1+ Nbre f

2 + Nbre f

⎝ ⎜ ⎜

⎠ ⎟ ⎟+ 0.75 ×

1+ Nbretn

2 + Nbret

⎝ ⎜

⎠ ⎟

Questions (3)

2 + Nbret

P Spam | Wn = true[ ]( ) =P Spam( ) × P Wn = true[ ]( )

P Spam( ) × P Wn = true[ ]( )Spam

Question (4)

P Spam |W0 ∧ ... WN−1( ) =

P Spam( ) × P Wn | Spam( )n=0

∏Spam

P Spam = true[ ] |W0 ∧ ... ∧WN−1( )P Spam = false[ ] |W0 ∧ ... ∧WN−1( )

=P Spam = true[ ]( )P Spam = false[ ]( )

×P Wn | Spam = true[ ]( )P Wn | Spam = false[ ]( )n=0

Bayesian Program = Description + Question

Utilization

SpecificationSpecification

IdentificationIdentification

• Variables

• Parametrical Forms or Recursive Question

• Decomposition

Preliminary Knowledge

Experimental Data

Bayesian Program = Description + Question

Results

SpamSievehttp://c-command.com/spamsieve/

Theoretical Basis

Content:• Definitions and notations• Inference rules• Bayesian program• Model specification• Model identification• Model utilization

Logical Proposition

Logical Proposition are denoted by lowercase name:

Usual logical operators:

Probability of Logical Proposition

We assume that to assign a probability to a given proposition a, it is necessary to have at least some preliminary knowledge, summed up by a proposition

P a | π( ) ∈ 0,1[ ]

Of course, we will be interested in reasoning on the probabilities of the conjunctions, disjunctions and negations of propositions, denoted, respectively, by:

P a∧b|π( ) P a∨b|π( ) P ¬a|π( )

We will also be interested in the probability of proposition aconditioned by both the preliminary knowledge and some other proposition b:

P a|b∧π( )

Normalization and Conjunction Postulates

Bayes ruleCox Theorem

Resolution PrincipleWhy don't you take the disjunction rule as an axiom?

P a∧b | π( ) = P a | π( ) × P b | a∧π( )

= P b | π( ) × P a | b∧π( )

P a | π( ) + P ¬ a | π( ) =1

P a∨b | π( ) = P a | π( ) + P b | π( ) − P a∧b | π( )

Discrete Variable• Variable are denoted by name starting

with one uppercase letter: • By definition a discrete variable is a

set of propositions – Mutually exclusive:– Exhaustive: at least one is true

The cardinal of X is denoted:

i ≠ j ⇒ xi ∧x j[ ] = false

X⎣ ⎦

Variable Conjunction

X∧Y = xi ∧y j{ }

X∨Y = xi ∨y j{ }Not a

variab

Conjunction rule

∀xi ∈ X,∀y j ∈Y

P xi ∧y j | π( ) = P xi | π( ) × P y j | xi ∧π( )

= P y j | π( ) × P xi | y j ∧π( )

P X∧Y | π( ) = P X | π( ) × P Y | X∧π( )

= P Y | π( ) × P X |Y ∧π( )

Normalization rule

PX∑ X | π( ) =1 Proof

Marginalization rule

P X∧Y | π( )X∑ = P Y | π( ) Proof

Contraction/Expansion rule

X∧Y = A ⇒ P X∧Y | π( ) = P A | π( )

P X∧Y | π( ) = P X | π( ) × P Y | X∧π( )

= P Y | π( ) × P X |Y ∧π( )

PX∑ X | π( ) =1

P X∧Y | π( )X∑ = P Y | π( )

X∧Y = A ⇒ P X∧Y | π( ) = P A | π( )

DescriptionThe purpose of a description is to specify an effective method to compute a joint distribution on a set of variables:

X1, X 2 ,..., X n{ }

Given some preliminary knowledge and a set of experimental data . This joint distribution is denoted as:

P X1∧X 2 ∧...∧X n | δ∧π( )

Spam,W0 , ...,WN−1{ }

P Spam∧W0 ∧ ... ∧WN−1 |δ∧π( )

DecompositionPartion in K subsets:

Li = X i1 ∧X i2 ∧...

P X1∧X 2 ∧...∧X n |δ∧π( )

= P L1 | δ∧π( ) × P L2 | L1∧δ∧π( ) ×...× P Lk | Lk−1∧...∧L1∧δ∧π( )

Conjunction rule:

P Li | Li−1∧...∧L1∧δ∧π( )

= P Li | Ri ∧δ∧π( )

P X1∧X 2 ∧...∧X n | δ∧π( )

= P L1 | δ∧π( ) × P L2 | R2 ∧δ∧π( ) ×...× P Lk | Rk ∧δ∧π( )

Conditional independance:

Decomposition:

L1 = Spam

L2 =W0

P Spam∧W0 ∧ ... ∧WN−1( )

= P Spam( ) × P W0 | Spam( ) × P W1 |W0 ∧Spam( )

× ... × P WN−1 |WN−2 ∧ ... ∧W0 ∧Spam( )€

LN+1 =WN−1

P Wn |Wn−1∧ ... ∧W0 ∧Spam( ) = P Wn | Spam( )

P Spam∧W0 ∧ ... ∧WN−1( )

Parametrical Forms or Recursive Questions

P Li | Ri ∧δ∧π( ) = fμ Ri ,δ( )

Parametrical form:

P Li | Ri ∧δ∧π( ) = P Li | Ri ∧ ′ δ ∧ ′ π ( )

Recursive Question:

P Wn | Spam∧δ∧π( ) ← Laplace succession law

QuestionGiven a description, a question is obtained by partitionning the set of variables into 3 subsets: the searched variables, the known variables and the free variables.

We define the Search, Known and Free as the conjunctions of the variables belonging to these three sets.

We define the corresponding question as the distribution:

P Search | Known∧δ∧π( )

Inference

P Search | Known∧δ∧π( ) = P Search∧Free | Known∧δ∧π( )Free∑

P Search∧Unknown∧Free | δ∧π( )Free∑

P Free | δ∧π( )

P Search∧Free∧Known | δ∧π( )Free∑

P Search∧Free∧Known | δ∧π( )Search∧Free

× P Search∧Free∧Known | δ∧π( )Free∑

2 optimisation problems

Draw P Search | Known∧δ∧π( )( )

P Search | Known∧δ∧π( )

× P Search∧Known∧Unknown |δ∧π( )Free∑

API and Inference Enginemain (){

//Variables plFloat read_time; plIntegerType id_type(0,1); plFloat times[5] = {1,2,3,5,10}; plSparseType time_type(5,times); plSymbol id("id",id_type); plSymbol time("time",time_type);

//Parametrical forms //Construction of P(id) plProbValue id_dist[2] = {0.75,0.25}; plProbTable P_id(id,id_dist);

//Construction of P(time | id = john) plProbValue t_john_dist[5] = {20,30,10,5,2}; plProbTable P_t_john(time,t_john_dist); //Construction of P(time | id = bill) plProbValue t_bill_dist[5] = {2,6,10,40,20}; plProbTable P_t_bill(time,t_bill_dist); //Construction de P(time | id) plKernelTable Pt_id(time,id); plValues t_and_id(time^id); t_and_id[id] = 0; Pt_id.push(P_t_john,t_and_id); t_and_id[id] = 1;

Pt_id.push(P_t_bill,t_and_id); //Decomposition // P(time id) = P(id) P(time | id) plJointDistribution jd(time^id,P_id*Pt_id);

Bayesi

Question

Specification

Identification

•Variables

•Decomposition

•Parametric Forms

•Learning from instances

//Question //Getting the question P(id | time) plCndKernel Pid_t; jd.ask(Pid_t,id,time);

//Read a time from the key board cout<<"P(id,time)= "<<Pid_t<<"\n"; cout<<"Time? : "; cin>>read_time;

//Getting P(id | time = read_time) plKernel Pid_readTime;

ProBT®ProBAYES.comBayesian-Programming.org

Pierre Bessière LPPA – Collège de France - CNRS Cours « Cognition bayésienne » 2010 The most...

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