Option Pricing Model Calibration In Practice

I'm curious how an option pricing model like the Heston model is calibrated in practice.

Here's how I imagine it happens:

Let's say I have access to the most recent option prices on a given stock for $n$ strikes and $m$ expirations dates for each strike. Let now say it's the end of the business day, so I have a set of $n \times m$ closing prices $p$ each given by $p(K,\tau)$. The collection of all closing prices would be denoted $\{p(K_i,\tau_j)\}_{i,j=1}^{n,m}$.

I'm going to need to price options on this stock tomorrow, so let's calibrate Heston's model to this most recent closing data. For this, use some method of least squares optimization, finding the parameter values that best describes the price data. Whatever method I use, I calibrate Heston's model to the $n \times m$ data points of today.

Now, let's say I also have access to previous days' closing prices, say $d$ days, each with $n \times m$ prices. That's $d \times n \times m$ closing prices.

1. 
   

   Should I include all $d \times n \times m$ closing prices in my calibration? That is, should I try to fit Heston's model to $d \times n \times m$ data points? This seems like it would better capture the recent history of the option prices.

   
   


2. 
   

   Or, should I stick with the most recent data, perhaps relying on some efficient markets idea to argue this is all I need?

   
   


3. 
   
   

   Or, perhaps calibrate the model $d$ times, once to each day of data, build a distribution of parameters and pick the most likely parameters?

   
   


4. 
   

   Or,...

   
   

It seems there are many methods for calibration. I'd be excited to hear from those with experience on how they have seen it done!