Latent Calendar: Modeling Weekly Behavior with Latent Components

We will delve into how Latent Dirichlet Allocation can be applied to discretized calendar events, allowing us to tap into the model's probabilistic origins and its connection to Bayesian principles, offering a wide array of potential applications and insights.


AUTHORED BY

William Dean

DATE

2023-10-27


Introduction

In this webinar, we will explore the use of a traditional Natural Language Processing technique for modeling weekly calendar data. We will delve into how Latent Dirichlet Allocation can be applied to discretized calendar events, allowing us to tap into the model's probabilistic origins and its connection to Bayesian principles, offering a wide array of potential applications and insights.

About Speaker

Will Dean is a Statistician and Data Scientist with experience in geospatial and user analytics. He is passionate about Bayesian methods and using data visualization to tell a story. He is interested in software design and how it can make data problems easier and more enjoyable to solve.

Timestamps

00:00 Webinar begins

04:00 Presentation begins

04:29 Will's background

05:11 About the talk

05:57 Case study dataset

06:57 Data in mind

07:33 Timestamps provide more info

07:52 Calendar Visualization

09:50 Data Generation Process (First Attempt)

11:26 Data Generation Process (Second Attempt)

12:32 Discrete Approximation

13:19 Data Generation Process (Third Attempt)

13:57 How to people get around?

13:58 Data Generation Process (Fourth Attempt)

17:41 Latent Dirichlet Allocation

18:26 Use what is available

19:51 Define "Vocab"from timestamps

20:23 Aggregate to "documents"

21:42 Learn from "topics"

23:43 "Topic" insights

24:12 Predict and Transform

26:01 Low data support

27:24 Prior Impact

29:27 Next steps for latent-calendar project

30:44 Where it fits into marketing?

33:57 (Q/A) How was the project perceived by stakeholders?

38:10 (Q/A) Is this being used primarily as an insight generating tool?

39:48 (Q/A) Can you explain the connection to CLV modelling ...

44:14 (Q/A) In general more about properties and exploiting them when modelling ...

53:34 (Q/A) Whenever working with Timestamp data is it cyclical ....?

55:40 Webinar ends

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Work with PyMC Labs

If you are interested in seeing what we at PyMC Labs can do for you, then please email info@pymc-labs.com. We work with companies at a variety of scales and with varying levels of existing modeling capacity. We also run corporate workshop training events and can provide sessions ranging from introduction to Bayes to more advanced topics.