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I'm going through the Price Is Right example in chapter 5 of Probabilistic Programming & Bayesian Methods for Hackers.

It reads:

Example: Optimizing for the Showcase on The Price is Right

Bless you if you are ever chosen as a contestant on the Price is Right, for here we will show you how to optimize your final price on the Showcase. For those who forget the rules:

  1. Two contestants compete in The Showcase.
  2. Each contestant is shown a unique suite of prizes.
  3. After the viewing, the contestants are asked to bid on the price for their unique suite of prizes.
  4. If a bid price is over the actual price, the bid's owner is disqualified from winning.
  5. If a bid price is under the true price by less than \$250, the winner is awarded both prizes.

The difficulty in the game is balancing your uncertainty in the prices, keeping your bid low enough so as to not bid over, and trying to bid close to the price.

Suppose we have recorded the Showcases from previous The Price is Right episodes and have prior beliefs about what distribution the true price follows. For simplicity, suppose it follows a Normal:

$$\text{True Price} \sim \text{Normal}(\mu_p, \sigma_p )$$

In a later chapter, we will actually use real Price is Right Showcase data to form the historical prior, but this requires some advanced PyMC3 use so we will not use it here. For now, we will assume $\mu_p = > 35 000$ and $\sigma_p = 7500$.

We need a model of how we should be playing the Showcase. For each prize in the prize suite, we have an idea of what it might cost, but this guess could differ significantly from the true price. (Couple this with increased pressure being onstage and you can see why some bids are so wildly off). Let's suppose your beliefs about the prices of prizes also follow Normal distributions:

$$ \text{Prize}_i \sim \text{Normal}(\mu_i, \sigma_i ),\;\; i=1,2$$

This is really why Bayesian analysis is great: we can specify what we think a fair price is through the $\mu_i$ parameter, and express uncertainty of our guess in the $\sigma_i$ parameter.

We'll assume two prizes per suite for brevity, but this can be extended to any number. The true price of the prize suite is then given by $\text{Prize}_1 + \text{Prize}_2 + \epsilon$, where $\epsilon$ is some error term.

We are interested in the updated $\text{True Price}$ given we have observed both prizes and have belief distributions about them. We can perform this using PyMC3.

Lets make some values concrete. Suppose there are two prizes in the observed prize suite:

  1. A trip to wonderful Toronto, Canada!
  2. A lovely new snowblower!

We have some guesses about the true prices of these objects, but we are also pretty uncertain about them. I can express this uncertainty through the parameters of the Normals:

$$\begin{align}\text{snowblower} \sim \text{Normal}(3 000, 500 )\\\\\text{Toronto} \sim \text{Normal}(12 000, 3000 )\\\\\end{align}$$

For example, I believe that the true price of the trip to Toronto is 12 000 dollars, and that there is a 68.2% chance the price falls 1 standard deviation away from this, i.e. my confidence is that there is a 68.2% chance the trip is in [9 000, 15 000].

The code that was provided is the following:

import pymc3 as pm

data_mu = [3e3, 12e3]

data_std = [5e2, 3e3] 

mu_prior = 35e3
std_prior = 75e2

with pm.Model() as model:

    true_price = pm.Normal("true_price", mu=mu_prior, sd=std_prior)

    prize_1 = pm.Normal("first_prize", mu=data_mu[0], sd=data_std[0])
    prize_2 = pm.Normal("second_prize", mu=data_mu[1], sd=data_std[1])
    price_estimate = prize_1 + prize_2

    logp = pm.Normal.dist(mu=price_estimate, sd=(3e3)).logp(true_price)
    error = pm.Potential("error", logp)

    trace = pm.sample(50000, step=pm.Metropolis())
    burned_trace = trace[10000:]

price_trace = burned_trace["true_price"]

I don't understand:

  1. How does the true_price fit in with price_estimate?
  2. Where did sd=(3e3) come from?
  3. What is a pm.Potential object?

Any help would greatly be appreciated. Thanks!

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  • $\begingroup$ You need to give more context what problem that piece of code tries to solve, without forcing people to go to the link and search it there. Also with respect to pymc3.Potential -- what it does it just adds arbitrary functions to the log(posterior). In the example given logp is a function of true_price, prize_1,prize_2 and pymc.Potential will add this function to the log(post) $\endgroup$ – sega_sai Dec 13 '16 at 12:23
  • $\begingroup$ Thanks, @sega_sai. I updated the question with text from the textbook. Can you elaborate more about the motivation behind adding an error to logp? $\endgroup$ – JPN Dec 14 '16 at 0:53
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We use pm.Potential here primarily to get around the definition of a likelihood. We ordinarily use it to constrain our likelihood in the manner described in the PyMC docs, but in this example we never end up defining a true likelihood (which would require the inclusion of observations). As such, all the samples that we draw are based on how we defined the potential.

Our price_estimate and true_price are related to each other in the potential by essentially making our true_price the observed values. When we say:

logp = pm.Normal.dist(mu=price_estimate, sd=(3e3)).logp(true_price)

We are evaluating a normal distribution with mean of price_estimate, standard devation of 3e3, at the values provided by true_price (our mock observations). This simulates a likelihood that we can then sample from to get our posteriors. As for the validity of 3e3 as a the standard deviation, I think it is reasonable, given that it is the larger of the standard deviations that we used to define the components of our price_estimate here:

data_std = [5e2, 3e3]

I kept "error" as the name of the variable because that's how Cam named the function when he used the pm.potential decorator in the PyMC version of this chapter.

Please let me know if this is unclear!

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  • $\begingroup$ This is a bit of a black sheep model, due to the total substitution of the likelihood with a potential. We should use pm.DensityDist to define a custom likelihood, but I decided to replicate Cam's model as closely as possible. This is partially because I did not have the appropriate observations to construct the likelihood as we normally should. $\endgroup$ – Max Margenot Dec 20 '16 at 19:47
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There is a description of potentials in the old version of PyMC documentation:

http://pymc-devs.github.io/pymc/modelbuilding.html#the-potential-class

From what I understand, probabilistic programming consists of Monte Carlo simulating in areas of high likelihood. Usually this likelihood is determined by setting the likelihood by defining a distribution and setting the observed argument with realized data.

So for example, in the linear regression example in the PyMC3 doc.

basic_model = Model()

with basic_model:

    # Priors for unknown model parameters
    alpha = Normal('alpha', mu=0, sd=10)
    beta = Normal('beta', mu=0, sd=10, shape=2)
    sigma = HalfNormal('sigma', sd=1)

    # Expected value of outcome
    mu = alpha + beta[0]*X1 + beta[1]*X2

    # Likelihood (sampling distribution) of observations
    Y_obs = Normal('Y_obs', mu=mu, sd=sigma, observed=Y)

Y_obs is what determines the likelihood function.

You can also set a custom likelihood function. That's where the potential comes in.

I believe the name error is misleading. It should be likelihood (which is the equivalent of the negative of the likelihood)

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