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Classical inference in/for physics

Thiago Mosqueiro
Thiago Mosqueiro
Thiago MosqueiroApplied Scientist at Amazon.com

A brief review of basics in Statistics and Classical Inference. This talk was given to a very specific public, interested in seeing how Statistics can be employed step-by-step. Especially, Maximum Likelihood estimators are discussed and applied to three simple data sets as a way to fit your probabilistic.

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Classical Inference
in Physics
Thiago Mosqueiro
Institute of Physics of S˜ao Carlos
University of S˜ao Paulo
July 31 2012
Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 1 / 38
Equilibrium criticality – Ising model
• Model of ferromagnetism in statistical
mechanics
• Contact with thermal reservoir
• Lattice of N binary elements – spins
• Each site of this lattice: or
Energy of a configuragion S = (S1, S2, . . .),
E(S) = −J
p,j
SjSp
Probability of S = (S1, S2, . . .),
P(S) =
exp − J
kT
p,j
SjSp
∀S
exp − J
kT
p,j
SjSp Monte Carlo step: each time step means N itera-
tions of the algorithm.
Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 2 / 38
The Market for Lemons: Quality Uncertainty and the Market Mechanism, G. Akerlof.
The quarterly journal of economics, 1970
Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 3 / 38
Objectivies
• What’s the difference between Probability and Statistics?
• Inference
• Statistics – models, hypotehsis and estimation
• Main focus: Maximum likelyhood estimators
• Glimpse of hypothesis testing
“What! you have solved it already?”
“Well, that would be too much to say.
I have discovered a suggestive fact, that is all. It is, however, very suggestive.”
Sign of Four (II), Sir Arthur Conan Doyle
Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 4 / 38
Summary
1 Probability and random variables
2 Estimators
3 Maximum likelihood
4 Non-trivial example
5 One last example
6 Hypothesis testing
7 Conclusions
Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 5 / 38
Where are we?
1 Probability and random variables
2 Estimators
3 Maximum likelihood
4 Non-trivial example
5 One last example
6 Hypothesis testing
7 Conclusions
Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 6 / 38
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Classical inference in/for physics

  • 1. Classical Inference in Physics Thiago Mosqueiro Institute of Physics of S˜ao Carlos University of S˜ao Paulo July 31 2012 Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 1 / 38
  • 2. Equilibrium criticality – Ising model • Model of ferromagnetism in statistical mechanics • Contact with thermal reservoir • Lattice of N binary elements – spins • Each site of this lattice: or Energy of a configuragion S = (S1, S2, . . .), E(S) = −J p,j SjSp Probability of S = (S1, S2, . . .), P(S) = exp − J kT p,j SjSp ∀S exp − J kT p,j SjSp Monte Carlo step: each time step means N itera- tions of the algorithm. Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 2 / 38
  • 3. The Market for Lemons: Quality Uncertainty and the Market Mechanism, G. Akerlof. The quarterly journal of economics, 1970 Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 3 / 38
  • 4. Objectivies • What’s the difference between Probability and Statistics? • Inference • Statistics – models, hypotehsis and estimation • Main focus: Maximum likelyhood estimators • Glimpse of hypothesis testing “What! you have solved it already?” “Well, that would be too much to say. I have discovered a suggestive fact, that is all. It is, however, very suggestive.” Sign of Four (II), Sir Arthur Conan Doyle Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 4 / 38
  • 5. Summary 1 Probability and random variables 2 Estimators 3 Maximum likelihood 4 Non-trivial example 5 One last example 6 Hypothesis testing 7 Conclusions Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 5 / 38
  • 6. Where are we? 1 Probability and random variables 2 Estimators 3 Maximum likelihood 4 Non-trivial example 5 One last example 6 Hypothesis testing 7 Conclusions Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 6 / 38
  • 7. Random variable • Let |ψ be the state of a particle • Suppose we know for every n the solutions H |n = n |n What is the probability the particle is in state |n ? • It can happen that |ψ = |1 . . . or |ψ = |2 . . . or . . . • In this sense, |ψ is a random variable • Suppose you have n dice Side j of each die have probability pj • The result of tossing the j-th die is Xj What is the probability of Xj > x? • Xj is another random variable Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 7 / 38
  • 8. Random variables • Events: X = x, X ≤ x, X = x and Y < y • Probability of an event: P (X = x), P (X ≤ x), P (X = x, Y < y) • Moments: X := x P(X = x)x, X2 := x P(X = x)x2 , . . . • Surprisal: I (X = x) = − log [P (X = x)] • Entropy: I (X = x) = − x P (X = x) log [P (X = x)] Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 8 / 38
  • 9. Example: gaussian distributed variable X ∼ N(µ, σ) P(x < X ≤ x + dx) = ρ(x)dx = A exp − (x − µ)2 σ2 dx - 3 - 2 - 1 φμ,σ2( 0.8 0.6 0.4 0.2 0.0 −5 −3 1 3 5 x 1.0 −1 0 2 4−2−4 x) 0,μ= 0,μ= 0,μ= −2,μ= 2 0.2,σ = 2 1.0,σ = 2 5.0,σ = 2 0.5,σ = Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 9 / 38
  • 10. Example: gaussian distributed variable X ∼ N(µ, σ) P(x < X ≤ x + dx) = ρ(x)dx = A exp − (x − µ)2 σ2 dx 0 1 2 3 4 5 6 0 100 200 300 400 500 600 700 EventXj Realization j Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 9 / 38
  • 11. Now the big problem... What if I don’t have a model...? How can one obtain information from observations? How can I know my model fits the reality? Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 10 / 38
  • 12. Where are we? 1 Probability and random variables 2 Estimators 3 Maximum likelihood 4 Non-trivial example 5 One last example 6 Hypothesis testing 7 Conclusions Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 11 / 38
  • 13. Statistically infering... • Statistics validates and fits Probabilistic models • build a statistical model that should describe the process • interpret the data as realizations of your model • Inference gives you a statistical proposition • Models may be parametric, non-parametric or semi-parametric • Of course let’s focus on parametric models. Let’s guess our model: Xj ∼ N(µ, σ) What’s your best guess about µ and σ? Usual way of doing this estimate is by means of an estimator 0 1 2 3 4 5 6 0 100 200 300 400 500 600 700 EventXj Realization j Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 12 / 38
  • 14. Estimator • Data: set of i.i.d. X1, X2, X3, . . . Xm • Given a data set and a statistical model, we have a probability with some parameter θ • An estimator is a function of the data set to some sample estimates Examples of estimators • X = 1 m m j=1 Xj • ˆσ = 1 m m j=1 Xj − X 2 • ˆxmin = min (X1, X2, X3, . . . Xm) Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 13 / 38
  • 15. Mean Square Error • EQM ˆθ := ˆθ − θ 2 • EQM ˆθ = ˆθ2 − 2θˆθ + θ2 = ˆθ2 − 2θ ˆθ + θ2 = ˆθ2 − ˆθ 2 + ˆθ 2 − 2θ ˆθ + θ2 EQM ˆθ = Var ˆθ − B2 (ˆθ) • Bias: B(ˆθ) = ˆθ − θ • Non-biased estimator: B(ˆθ) = 0 ⇐⇒ ˆθ = θ Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 14 / 38
  • 16. Example to gaussian mean and variance • Random sample: X1, X2, X3, . . . , Xm • Starting with the mean value: ˆµ = X := 1 m m j=1 Xj • ˆµ = 1 m m j=1 Xj = 1 m m j=1 Xj = 1 m m j=1 µ = µ Thus, X is a non-biased estimator for µ • Now let’s take a look at the • EQM [ˆµ] = Var X = Var 1 m m j=1 Xj = 1 m2 m j=1 Var [Xj] = 1 m2 m j=1 σ2 = σ2 m Moreover, X → µ when m → ∞ Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 15 / 38
  • 17. Example to gaussian mean and variance • Let’s now do it with the variance: ˆσ2 b = 1 m m j=1 Xj − X 2 • ˆσ2 b = 1 m m j=1 Xj − X 2 = 1 m m j=1 Xj − X 2 = m − 1 m σ2 Thus, ˆσb is asymptotically non-biased estimator for σ • Conversely, let’s define ˆσ2 = 1 m − 1 m j=1 Xj − X 2 • EQM [ˆµ] = σ2 ˆσ is an unbiased estimator for σ Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 16 / 38
  • 18. Back to Gaussian random variables 0 1 2 3 4 5 6 0 100 200 300 400 500 600 700 EventXj Realization j • Estimation: ˆσb = 0.49944735873 • Actual value used to generate the data: ˆσ = 0.49980448952 Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 17 / 38
  • 19. Where are we? 1 Probability and random variables 2 Estimators 3 Maximum likelihood 4 Non-trivial example 5 One last example 6 Hypothesis testing 7 Conclusions Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 18 / 38
  • 20. Maximum Likelihood • Maximize the ”likelihood“: get an estimator for te parameter of a given statistical model • p(x1, x2, x3, . . . |θ) :: given the value θ for the parameter, this is the probability that X1 = x1, X2 = x2, . . . • p(x1, x2, x3, . . . |θ) = p(x1|θ)p(x2|θ)p(x3|θ) . . . = m j=1 p(xj|θ) • Let x = x1, x2, x3, . . . L(θ, x) = m j=1 p(xj|θ) • To maximize it, we can use ln (L(θ, x)) = m j=1 p(xj|θ) • ∂ ∂θ ln (L(θ, x)) = 0 – solve it for θ Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 19 / 38
  • 21. Example for gaussian • Suppose Xj ∼ N(µ, σ) is our statistical model • L(ˆµ, x) = 1 √ 2πσ2 m exp 1 2σ2 m j=1 (xj − ˆµ)2 • By calculating ∂ ∂ˆµ ln (L(ˆµ, x)) = 0, we get to m j=1 (xj − ˆµ) = m j=1 xj − mˆµ = 0 • Finally, we get ˆµ = 1 m m j=1 xj • On the other hand, ∂ ∂ˆσ ln (L(ˆσ, x)) = 0 gives ........ • This is what is usually done to derive such an estimator Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 20 / 38
  • 22. Example for gaussian • Suppose Xj ∼ N(µ, σ) is our statistical model • L(ˆµ, x) = 1 √ 2πσ2 m exp 1 2σ2 m j=1 (xj − ˆµ)2 • By calculating ∂ ∂ˆµ ln (L(ˆµ, x)) = 0, we get to m j=1 (xj − ˆµ) = m j=1 xj − mˆµ = 0 • Finally, we get ˆµ = 1 m m j=1 xj • On the other hand, ∂ ∂ˆσ ln (L(ˆσ, x)) = 0 gives ˆσ = 1 m m j=1 Xj − X 2 , which we have already discovered to be biased! • This is what is usually done to derive such an estimator Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 20 / 38
  • 23. Deutsch Tank Problem • Suppose you have a box with (unkown) n tickets, labled from 1 to n. • You take one ticket, it’s label is x. What’s your best guess for n? Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 21 / 38
  • 24. Estimation of the maximum • Statistical model: p(x|ˆn) = 1 ˆn , x ≤ ˆn – a uniform distribution • Remember p(x|ˆn) = 0, x > ˆn. This is important to this maximization. • L(ˆn, x) = 1 ˆn , which is maximized when ˆn is the largest! • Therefore, our best guess at this moment would be ˆn = x. • If we had conversely made several observations X1, X2, X3, . . ., then ˆn = max (X1, X2, X3, . . .) • ”However, this is awful“ – it is a poor estimation! • Other methods are far more accurate and, indeed, were succesfully used in WWII. Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 22 / 38
  • 25. Where are we? 1 Probability and random variables 2 Estimators 3 Maximum likelihood 4 Non-trivial example 5 One last example 6 Hypothesis testing 7 Conclusions Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 23 / 38
  • 26. What is this? Large values significant? Is the mean informative? Probably not. 0 10 20 30 40 50 60 70 0 20 40 60 80 100 120 140 160 Xj Realization j Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 24 / 38
  • 27. Empirical PDF • We don’t know p(x)∆x, but we have a lot data... • In this experiment, I will use 107 points. • We can then calculate the following estimator: ˆp(x) = 1 m m j=1 δ (Xj ∈ [x, x + ∆x]) 10-8 10-7 10-6 10-5 10-4 10-3 10-2 10-1 100 100 101 102 103 104 P(X=x) Event X = x In fact, we can show that ˆp(x) is an unbiased estimator for p(x). Let’s propose then a model: p(x) ∼ x−α But... α =? Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 25 / 38
  • 28. Empirical CDF If you want to fit, use the CDF 10-7 10-6 10-5 10-4 10-3 10-2 10-1 100 100 101 102 103 104 105 P(X>=x) Event X = x However, how about a maximum likelihood estimator? Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 26 / 38
  • 29. Exponent estimator • General power-law distribution: p(x)dx = α − 1 xmin x xmin −α dx • We have to maximize the likelihood: L (ˆα|X1, . . . Xm) = m j=1 ˆα − 1 xmin Xj xmin −ˆα • ln [L (α|X1, . . . Xm)] = m j=1 ln(α − 1) − ln(xmin) − α ln Xj xmin • Maximizing it... ˆα = 1 + m m j=1 Xj/xmin Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 27 / 38
  • 30. PDF In our case, ˆα ∼ 2.65 10-8 10-7 10-6 10-5 10-4 10-3 10-2 10-1 100 100 101 102 103 104 P(X=x) Event X = x Testing data Fitting curve Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 28 / 38
  • 31. Where are we? 1 Probability and random variables 2 Estimators 3 Maximum likelihood 4 Non-trivial example 5 One last example 6 Hypothesis testing 7 Conclusions Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 29 / 38
  • 32. What about this... -250 -200 -150 -100 -50 0 50 100 150 0 50 100 150 200 250 300 EventXj Realization j Let’s start by the empirical pdf! Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 30 / 38
  • 33. Gaussian!!! :) Again, we can propose a model: A Gaussian! 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 -4 -2 0 2 4 P(X=x) Event X = x Estimating the variance: ˆσ ≈ 200 Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 31 / 38
  • 34. Gaussian!!! :( Again, we can propose a model: A Gaussian! 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 -4 -2 0 2 4 P(X=x) Event X = x Empirical PDF Fitting Estimating the variance: ˆσ ≈ 200 – Something is not right! Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 31 / 38
  • 35. Okay, not a gaussian 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 -10 -5 0 5 10 P(X=x) Event X = x Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 32 / 38
  • 36. Okay, not a gaussian 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 -20 -15 -10 -5 0 5 10 15 20 P(X=x) Event X = x Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 32 / 38
  • 37. Power-law decay – Cauchy distributions 1e-06 1e-05 0.0001 0.001 0.01 0.1 1 1 10 100 1000 10000 100000 P(X=x) Event X = x Empirical CDF Very suggestive indeed. Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 33 / 38
  • 38. Where are we? 1 Probability and random variables 2 Estimators 3 Maximum likelihood 4 Non-trivial example 5 One last example 6 Hypothesis testing 7 Conclusions Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 34 / 38
  • 39. Hypothesis testing • What we have been doing till now is ∼ exploratory analysis • We now want to confirm a prediction or hypothesis – confirmatory analysis • Is this last data set gaussian or cauchy distributed? • The general recipe: - An initial guess, possibly true - State an relevant null and its alternative hypothesis - Formulate an appropriate test – T and a significance level τ - Estimate the distribution of T under your null hypothesis - Compute the observed quantity t and verify your null hypothesis Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 35 / 38
  • 40. Let’s try this shit! • H0: the data is normally distributed – N(0, σ) • We have the estimated pdf of the data sample – these are our m observations Oj • The test will be T = m j=1 (Oj − Ej)2 Ej • Ej = A exp − (x)2 σ2 are the expected frequencies! • It is easy to derive that T ∼ χ2 m−1 (Bolfarine) • In our case, the observed t ≈ 400 and P (T = t) → 0. • This rejects H0. Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 36 / 38
  • 41. Suggestions • Other hypothesis testing techniques: * τ-Student test * minimax * Lagrange multiplier * Union-intersection * Fisher test * ... • Non-parametric testins, such as Kolmogorov-Smirnov Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 37 / 38
  • 42. Where are we? 1 Probability and random variables 2 Estimators 3 Maximum likelihood 4 Non-trivial example 5 One last example 6 Hypothesis testing 7 Conclusions Thiago Mosqueiro (IFSC - USP) Classical inference in physics 31/07/2012 38 / 38