You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Q:

Multivariate non-parametric density estimation with many missing values

Apologies in advance if any of my terminology here is wrong, I'm not an expert in statistics. If I've made any mistakes, let me know and I'll correct them.
The task I'm looking for some advice on approaching is parameterless density estimation (particularly kernel density estimation, but if somebody wants to suggest an entirely different approach I'd be interested in that too) in which there are many (100s or 1000s) of variables, but for each datapoint, I only know a small number of them (perhaps less than 100). 
The known variables are not flatly distributed, and in fact the value of one variable (known or unknown) may have an effect on how likely another variable is to be observed. Whether or not a particular variable is observed may also have an effect on how likely another variable is to be observed.
Assume here that I have an extremely large number of datapoints. It should also be mentioned that the more often a variable, or combination of variables, is observed, the more important it is for me to be able to predict it. So, for example, if there's a certain value of variable X for which I never observe variable Y, I don't care about being able to estimate Y for that value of X.
Bandwidth selection in this situation is also something I could use some guidance on, for the kernel approach. I'd be happy to hear about either theoretical approaches or R packages.

A:

In order to fit a multivariate kernel density estimate (KDE) with missing data (as it sounds like you are) you need to impute the missing data. Alternatively, you could try using Naive Bayes, which would allow you to fit a unique kernel density estimate per feature, per class, which would alleviate your missing data issue and not require any imputation. Note, Naive Bayes rests on the assumption that your features are independent (although often times we may make this assumption even when it isn't so and the method still works well). 
For the first approach, fitting a multivariate KDE with missing data, there are many imputation approaches available, some of which were designed for this purpose. This well-cited 2014 paper compares a few different approaches: Influence of Missing Values Substitutes on Multivariate Analysis of Metabolomics Data, Gromski et al. 
For the Naive Bayes approach, you'll need to make the assumption that your feautures are independent, thus redefining the original Bayes Rule:
\begin{equation}
P_M(t_k|\textbf{x}) = \frac{p(\textbf{x}|t_k) }{p(\textbf{x})} p(t_k)
\label{eq:nbmultimcprob}
\end{equation}
where $\textbf{x}={x_1, x_2,...,x_N}$ is your vector of features, $t_k$ is class $k$ (and implies a positive class instance $t_k=1$), $p(\textbf{x}|t_k)$ is the likelihood, which you can think of as the multivariate KDE across all features, $p(t_k)$ is the class prior (often based on class frequency of the training set), and  $p(\textbf{x})=\sum_{k=1}^K p\left(t_{k}\right) \prod_{i=1}^{N} p(x_i|t_k)$ is the evidence for $K$ transient classes. Feature independence converts this to Naive Bayes which is defined as:
\begin{equation}
    P_M(t_k|x_1, x_2,..., x_N) = \frac{ \prod_{i=1}^{N} p(x_i|t_k) } {p(\textbf{x})} p(t_k)
\end{equation}
where $p(\textbf{x}|t_k)$  has been replace by the product of probability densities from each feature's KDE: $\prod_{i=1}^{N} p(x_i|t_k)$. 
Lastly, you mention bandwidth-setting in your question. As is mentioned in this 2012 paper: Classification Using Kernel Density Estimates, Ghosh et al., bandwidth is often set uniquely for each class (and in the Naive Bayes setting, perhaps for each feature and each class). Bandwidths are often optimized using a grid search over a range of valid values (which is dictated by the values of the dataset, and whether or not you scale the data).