The book contains a chapter called ‘The Tears’. At its conclusion, John Glenn is

6 min readDec 3, 2020

Soulmates don’t exist. Real human beings do. The goal is to redefine this idea of the perfect kind of love, and turn it into an openness to a very real kind of love. You aren’t going to find someone who completes you, and you shouldn’t strive for that. Instead, strive to find someone who inspires you to complete yourself. Strive to find someone who pushes you, and breaks into you in ways that allow for you to see all of the potential that exists within your bones. Strive to find someone who your loves grows with — slowly, strongly. Strive to find someone who ignites within you the motivation to love in the most enduring of ways because, luckily, that kind of person does exist.

Just a quick note on the parameter σ_ε². We’ve been estimating the variance of the ε_t term. Nevertheless, when fitting our likelihood, we are using the most usual way to fit a Gaussian distribution with the mean and the standard deviation. That is why I created a Deterministic variable (nothing to do with our deterministic model), which is the way to keep track of a transformed variable in PyMC3.

When I was growing up I dedicated my heart, with reckless abandon, to the idea of finding my soulmate. Wistfully I listened to stories of the ancient gods, and how they split each and every single human being into two parts. We were one part, and the other half, our counterpart, would make their way into our lives at some point, completing us and pouring into us a love that we had never experienced before. Now I am realizing that such a philosophy is actually more harmful than it is valuable.Initially, I was ashamed of the change. It was as if a werewolf had bitten me. Instead of growing hairy at the full moon, I’d grow tearful at a beautiful night sky. Imagine if someone saw. My wife or, worse, another man? I was warned that parenthood would change me, but I thought that meant no longer having fun in all-night dance parties — that sort of thing. Crying wasn’t what real men did. Real men like astronauts, like the type described in Tom Wolfe’s 1979 The Right Stuff.

I do believe that we should feel something unique when we finally meet the person we choose to spend our lives with, and I do believe that we should be inspired and deeply moved by the love we are gifted in life. However, we are not incomplete human beings, and there is no one in the world who is going to fill the voids of our soul other than ourselves. That rush of feeling that we want to experience when we meet our soulmate is not a feeling that is bred from the stitching together of two reunited hearts, and it isn’t as tell tale as we often think it is.

These tears marked a profound shift. I had transformed; not a change of actor playing the same character but the same person playing a different role. From the day my son was born, I became a tearful father, ready to well up at the drop of a hat — literally — if someone, especially a child, dropped their little hat, I’d break down. What if they lost it? What if their tiny head became cold? Writing this, my eyes are watering. Because I cry at anything and everything.Unobserved Components Results
==================================================================================
Dep. Variable: y No. Observations: 192
Model: deterministic constant Log Likelihood 63.314
Date: Wed, 25 Nov 2020 AIC -124.628
Time: 16:24:36 BIC -121.375
Sample: 0 HQIC -123.310
— 192
Covariance Type: opg
====================================================================================
coef std err z P>|z| [0.025 0.975]
— — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — —
sigma2.irregular 0.0294 0.003 8.987 0.000 0.023 0.036
===================================================================================
Ljung-Box (Q): 637.74 Jarque-Bera (JB): 0.73
Prob(Q): 0.00 Prob(JB): 0.69
Heteroskedasticity (H): 2.06 Skew: 0.09
Prob(H) (two-sided): 0.00 Kurtosis: 2.76
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).

I didn’t cry when my son was born. He did, but I didn’t. It was a shrill sound, more a broken toy than a human. Me, I was too stunned to cry. Later, as his mother recovered in the ward, I looked down at this tiny, helpless form. I didn’t know how I’d cope, what I’d do, if this new future could possibly work. After months of waiting, his arrival came suddenly, a summer shower. Standing there, I felt a strange emptiness. ‘What now?’ I remember thinking, and the size of the question frightened me.

As simple as that, we have our model fitting the data. We can see our two parameters sigma2.irregular (ε_t) and the level component μ_1. We also get several statistical tests that we will learn about in the future.

In reality, there is more to forever than simply finding someone who you feel extremely connected to. We have to stop allowing this surge of feeling to act like a compass towards healthy relationships. Distinctive and enchanting feelings for someone are a great compass, but they shouldn’t be the only thing we take into account when falling in love. We cant keep striving for this absolute, perfect love, because when life throws us some less than perfect situations we are going to need more than connection, more than an emotional upwelling of exhilaration. We are going to need pragmatic love, we are going to need levelheadedness, we are going to need a person who fights for us every single day — who chooses us in spite of circumstances.

In this estimation, we will not be focusing so much on our Bayesian workflow and using all the good practices that we learned in the last post. But don’t worry, we will get back to it in the future. The reason behind it is our intention of showing that the MLE and the MAP are indeed the same thing, if we use flat priors. We will be using very vague priors (not actually flat, to help our sampler slightly).

Let’s follow the same approach that we did in the last post. But first, we need to compare our Bayesian estimation of the parameters with the frequentist approach that we used earlier. Remember that we can solely compare pointwise estimations since only the Bayesian framework produces posterior distributions.

Not so exciting! The deterministic level model is constant, and thus it does not vary over time as a result. The residuals are clearly not randomly distributed for this case as they result from the deviations of the observed values from their mean.

We are building momentum and gathering important tools for the journey ahead. I think we are ready to start adding some stochastic behavior to our parameters. See you in the next post!

And here we have our parameters. For now, focus only on the columns mean and sd from the table above. As you can see, we have posterior distributions for our parameters μ_1 and σ_ε², not only a point estimate. Let’s compare our results with the ones that we got from our implementation using statsmodel.

The book contains a chapter called ‘The Tears’. At its conclusion, John Glenn is

Written by Sannan Mitha