PSM1 recap and outlook

PSM2 UCL

Bennett Kleinberg

8 Jan 2019

Welcome

Probability, Statistics & Modeling II

Lecture 1

Quick recap 1

Predicting crimes

Predicting crimes

Behind the problem:

What is the claim?

Formalising the problem

chance_day_1 = 0.5
chance_day_2 = 0.5
chance_day_3 = 0.5
#...

Solving the problem

Probability for correct prediction?

P(prediction == 1) = p_correct = 0.5

… on 10 consecutive days?

p_correct * p_correct * p_correct ...

p_correct = 0.5

# for d = 10 days

d = 10

#Formal:
p_correct ^ d
## [1] 0.0009765625

Equivalent to: 1/2^10 = 1/1024

MARGINAL Probability:

P(EVENT)

Even very, very, rare events happen

… but most of the time they don’t.

You need probability theory to tell the lucky from the likely.

(and proper statistics notations)

Quick recap 2

About Maria

Maria is 26 years old, single, outspoken, and very bright. She majored in law. As a student, she was deeply concerned with issues of discrimination and miscarriage of justice, and also participated in animal-rights demonstrations.

Adapted from Tversky & Kahneman (1983)

Which is more probable?

  • A: Maria works in a law firm
  • B: Maria works in a law firm and does pro bono work for disadvantaged defendants

Formalising the problem

Two events:

  • P(A) #prob of answer A
  • P(B) #prob of answer B

… BUT:

There’s something special with P(B)

P(B) = P(A) + "something else"

P(B) contains two ‘events’: P(A) and ‘pro bono work’

Let 'pro bono work' be P(C)

P(B) = P(A) and P(C)

Solving the problem

Joint probability

P(B) = P(A and C)

Let’s try:

Prob_A = 0.4
Prob_C = 0.3

Formula: P(A and B) = P(A)*P(C)

(Prob_A_and_C = Prob_A * Prob_C)
## [1] 0.12

By definition: P(X) > P(X and Y)

Therefore:

P(‘M is a lawyer’) > P(‘M is a lawyer’ and ‘pro-bono work’)

JOINT Probability:

P(EVENT_A AND EVENT_B) = P(EVENT_A)*P(EVENT_B)

Probability of two independent events is always smaller than the probability of each single events.

Quick recap 3

Screening terrorists

Your turn

What are the chances that this man is a terrorist?

Formalising the problem

CONDITIONAL Probability:


Probability of TERRORIST given that there is an ALARM


Looking for: P(terrorist GIVEN alarm)

Formal: P(terrorist|alarm)

Solving the problem (method 1)

Terrorist Passenger
Terrorist 950 50 1,000
Passenger 4,950 94,050 99,000
5,900 94,100 100,000

P(terrorist|alarm) = 950/5900 = 16.10%

Solving the problem (method 2)

Bayes’ rule

Setting the stage:

  • P(T) -> probability of terrorist
  • P(A) -> probability of alarm

We want:

  • P(T|A)

We know:

  • accuracy = P(A|T) = 0.95
  • baserate = P(T) = 0.01

Bayes’ rule (cont’d)

accuracy = 0.95 #P(A|T)
baserate = 0.01 #P(T)

Bayes’ rule: P(T|A) = ( P(A|T) * P(T) ) / P(A)

P(A) –> probability of any alarm???

P(A) = P(A|T) * P(T) + P(A|notT) * P(notT)

(Prob_notT = 1 - baserate) #P(notT) = 1 - P(T)
## [1] 0.99
(Prob_A_given_notT = 1 - accuracy) #P(A|notT) = 1 = P(A|T)
## [1] 0.05

Bayes’ rule (cont’d)

Putting it together:

#Bayes' rule:
Prob_A = accuracy * baserate + Prob_A_given_notT * Prob_notT #P(A) = P(A|T) * P(T) + P(A|notT) * P(notT)
Prob_A
## [1] 0.059
Prob_T_given_A = (accuracy * baserate) / Prob_A #P(T|A) = ( P(A|T) * P(T) ) / P(A)
Prob_T_given_A
## [1] 0.1610169

! Revise this rule here

CONDITIONAL Probability:

P(EVENT_A GIVEN EVENT_B) = P(EVENT_A|EVENT_B)

Probability of one event given that another event is true.

BEWARE OF THE BASERATE FALLACY

Quick recap 4

Solving gang crime

The context

Problem: gang crime in London

Mayor proposes two programmes:

  • A: zero-tolerance
  • B: work-and-integration

100 gang-members in two areas.

Outcome measure: number of gang members who disengaged

Results

Programme A Programme B
Camden 63/90 8/10
Lambeth 4/10 45/90


Mayor has GBP 5m to invest in one programme.

Your decision?

Solving the problem

Programme A Programme B
Camden 63/90 = 70% 8/10 = 80%
Lambeth 4/10 = 40% 45/90= 50%
67/100 = 67% 53/100 = 53%

[a] phenomenon wherein an association or a trend observed in the data at the level of the entire population disappears or even reverses when data is disaggregated by its underlying subgroups Alipourfard et al., 2018

Simpson’s Paradox and Interpreting Data

Simpson’s paradox on YouTube

BEWARE OF THE CONTEXT OF YOUR DATA

10 min. break

This module

Aim

  • go beyond PSM I
  • understand more complex data
  • model data and make inferences
  • make sense of crime data

More on learning outcomes in the module handbook

Tools we’ll use

  • open-source + free
  • wide support community (e.g., on Stackoverflow)
  • made for statistics
  • state-of-the-art libraries

But still…

  • R grows fast
  • Highly desirable/required in industry (Google, Facebook, Microsoft, Amazon, …)

Structure of the module

  • 9 Lectures (Tuesdays, 14-16h)
  • 5 Tutorials (alternating Tuesdays, 10-12h)

Teaching assistant: Isabelle van der Vegt

Assessment

  • Class test
  • Applied Crime Analysis Project

Class test

  • 50% of final grade
  • 1-hour closed-book exam
  • 8 open questions & MC questions
  • Date: 19 Mar 2019, 14-16h, (details)

Applied Crime Analysis Project

  • 50% of final grade
  • apply skills on dataset
  • address a research question
  • demonstrate open science practices
  • Due: 29 Mar 2019 (details)

Outlook

  • The Generalised Linear Model
  • Non-parametric data + discrete data
  • Open Science lab
  • Statistical evidence
  • Bayesian statistics

What’s next?

Homework for today:

  1. Getting ready for R (on Moodle)
  2. R for Crime Scientists in 12 Steps

Next week:

Tutorial + lecture

Tutorial: Refresher of PSM I with R + GLM tutorial