Hi BA, I've been doing research for weeks and am struggling to figure out what parts I need to get the most juice out of my $500. I want a system primarily for music listening with movies and production or mixing secondary (so do not NEED a flat signature) I'll be moving the system between a desk and living room.
room specs are 1,000 sq ft and would like enough sound for a small 10 ish person party. Will mainly be plugging in a 3.5 mm but would like the option to bluetooth in preferably from a DAC.
Should I just get a DAC and 2 bookshelf speaker combo? Do I bother with an amp or just get the best powered speakers I can? WHen's a good time to buy speakers?
Thank you for any help!
EDIT: PARTY NOW FULL THANK YOU.
Hello there,
I'm a DM (1.6 YEARS XP)/ newbie player (6 sessions as a PC), looking for a group to either DM for or play with as a PC.
I generally don't mind what we do, however my experience is generally with D&D 5E, I am open to new settings and systems so please don't exclude anything you might want to run.
I've ran homebrewed campaigns (50/50, RP/combat split) mostly, however I did dabble in a session zero for Ravenloft, before the party fell apart due to conflicting commitments. I've also done a few sessions with a local group as a PC in a homebrewed campaign but again unfortunately due to conflicting commitments that too fell apart rather quickly.
If anybody is interested in either adoption for myself as a PC or if anybody wants to join a homebrew setting I've made please DM me or comment below.
I mainly use roll20 and Skype however I'm open to anything that gets us playing.
EVERYBODY IS WELCOME SO PLEASE BE WELCOMING, NO DISCRIMINATION OR PAID SESSIONS.
Hi all
In my signals and systems course, we have been given the following difference equation:
π¦[π] = π₯[π] + π¦[π β 1] + π¦[π + 2]
Which we have shown to be both unstable and non-causal. We were given the question "what is the purpose of this system". Since it is both unstable and non-causal, I would believe that it does not have a purpose, as I cannot find a useful purpose for such a system, but maybe I am missing something?
August 14 - 15, 2021
---
Submission deadline (extended): May 27, 2021, anywhere on Earth
Format: 3 page extended abstract + references + appendices, ACM Proceeding Template
Submission website: https://cmt3.research.microsoft.com/BCIRWIS2021
---
Increasingly we use machine learning to build interactive systems that learn from past actions and the reward obtained. Theory suggests several possible approaches, such as contextual bandits, reinforcement learning, the do-calculus, or plain old Bayesian decision theory. What are the most theoretically appropriate and practical approaches to doing causal inference for interactive systems?
We are particularly interested in case studies of applying machine learning methods to interactive systems that did or did not use Bayesian or likelihood based methods, with a discussion about why this choice was made in terms of practical or theoretical arguments. We also welcome submissions in the following areas:
- Offline evaluation of recommender and interactive systems.
- Comparison of Bayesian, off-policy and other heuristic approaches for offline metrics.
- Probabilistic approaches applied to contextual bandits and reinforcement learning approaches.
- Probabilistic approaches to incrementality and attribution.
- Non-Bayesian approaches and trade-offs with Bayesian/Likelihood approaches.
- Bayesian methods in a production environment.
Organizers
- Nicholas Chopin (ENSAE)
- Mike Gartrell (Criteo AI Lab)
- Dawen Liang (Netflix)
- Alberto Lumbreras (Criteo AI Lab)
- David Rohde (Criteo AI Lab)
- Yixin Wang (UC Berkeley)
I have posted a picture of the question and the 4 possible answers but I can't for the life of my figure it out. any help would be greatly appreciated!
https://preview.redd.it/alu1pcp16tu61.png?width=616&format=png&auto=webp&s=2fe4e7a01ad513f799119862bc270e07f24b838a
Racial discrimination, given it's immense relevance in today's political discourse as well as it's longstanding role in the United Statesβ history, has been the subject of an immense amount of research in economics.
Questions like "what is the causal effect of race on the probability of receiving a loan?" and, with renewed fervor in recent years questions like "what is the effect of race on things like police use of force, probability of being arrested, and conditional on being arrested, what's the probability of being prosecuted?". This R1 is about https://5harad.com/papers/post-treatment-bias.pdf (Goel et al from now on), which is itself a rebuttal to https://scholar.princeton.edu/sites/default/files/jmummolo/files/klm.pdf, (Mummolo et al) which is itself a rebuttal to papers like https://scholar.harvard.edu/fryer/publications/empirical-analysis-racial-differences-police-use-force (Freyer) which try to estimate the role of race in police use of force.
Mummolo et al is making the argument that common causal estimates of the effect of race on police-related outcomes are biased. Fivethirtyeight does a good job outlining the case here https://fivethirtyeight.com/features/why-statistics-dont-capture-the-full-extent-of-the-systemic-bias-in-policing/ but the basic idea is that if you believe that police are more likely to arrest minorities then your set of arrest records is a biased sample and will produce biased estimates of the effect of race on police-related outcomes.
The paper I am R1ing is about the question "conditional on being arrested, what is the effect of race on the probability of being prosecuted?" Goel et al use a set of covariates, including data from the police report and the arresteeβs race to try and get a causal estimate of the effect of race on the decision to prosecute. They claim that the problems outlined by Mummolo et al do not apply. They cite that in their sample, conditional on the details in the police report, White people who are arrested are prosecuted 51% of the time, while Black people are prosecuted 50% of the time. They use this to argue that there is a limited effect of race o
... keep reading on reddit β‘If you take the adage that, for every effect there is a preceding cause (without exception), to it's root, you'll discover there are only two possibilities.
One, that the number of causes stretches back infinitely with no real beginning having ever existed. This may seem unintuitive, but it's logically satisfying in that the adage referenced above holds true. It's sort of like paddling up a river in a search of it's source, only to find that the river regresses without end, no matter how far you go upstream.
The second possibility is that we exist in a causality loop, where once again there is no real beginning, nor is there an end. Simply put, A causes B, B causes C and finally C causes A. This is a self-referencing loop that can never be created and has simply always existed. It is doomed to repeat itself ad infinitum in exactly the same fashion with each iteration.
With the rules of hard determinism in place and any arguments contrary to those rules aside, how does everyone feel about this? Do you prefer one possibility over the other, and if so, why?
And the same for anticausal signals and anticausal systems?
Are there acausal signals?
Hey guys,
Can someone please explain how "x = 1/|t|" is a non - causal system? I understand what causal and non causal systems are but I am not sure how to check whether a particular system is causal or non - causal?
Thank you!
This is ridiculous . Online games usually have a casual mode and a competitive mode . Causal mode are people who are of similar account level or elo should be pair together . Why isn't their account level on this game ? The player level doesn't count cause it's for shards . There should be a rank mode where emblem system takes place and for causal mode it doesn't cause people will be pair of similar level . This tank reset shit is messy . Hardcore killers playing against noobs is not fun, vice versa.
I've been recently reading Judea's Pearl book "Causality Models Reasoning and Inference" and at a point he mentions:
> Finally, there is an additional advantage to basing prediction models on causal mechanisms that stems from considerations of stability (Section 1.3.2). When some conditions in the environment undergo change, it is usually only a few causal mechanisms that are affected by the change; the rest remain unaltered. It is simpler then to reassess (judgmentally) or reestimate (statistically) the model parameters knowing that the corresponding symbolic change is also local, involving just a few parameters, than to reestimate the entire model from scratch.
With the Footnote:
> To the best of my knowledge, this aspect of causal models has not been studied formally; it is suggested here as a research topic for students of adaptive systems.
This looks like a really interesting and exciting research area. However, the book is not that recent (2nd edition is from 2009). So, this is a bit of a longshot, has any development happened in that? Does anyone know any name/article/book which relates to the intersection between these two areas?
For example, is y(t)=1 a causal system?
Edit: https://prnt.sc/ld24pu to give you guys some context, I was asked yesterday in a test about the causality and stability of these systems. My answers were stable for all of them, causal for b) and c) and non-causal for a) and d). I'm just not sure about b).
I have a normal life. Work, family, dog, etc.
My friends and I play 2 or 3 matches a day. (5-6 days a week) Ever since the patch that changed the loot system, none of us received anything.
Gaining levels became pointless. It's a new dissapointment each and every time. (except for that treasure, but I've received common/uncommon items from it lately)
I did't mind receiving random common/ucommon/rare items every once in a while. I liked it whatever it was. We could always trade them if we didn't need them anyway.
Now it's noting. And even if I received some item set after 2-3 months I wouldn't be too happy about it, because I'd know that there'll be nothing for the next few months.
Because Wikipedia's article on them are all grad student-level gobbledegook.
I need something more on the level of A Brief History of Time or Cosmos.
August 14 - 18, 2021 (final workshop date TBD)
---
Submission deadline (extended): May 20, 2021, anywhere on Earth
Format: 3 page extended abstract + references + appendices, ACM Proceeding Template
Submission website: https://cmt3.research.microsoft.com/BCIRWIS2021
---
Increasingly we use machine learning to build interactive systems that learn from past actions and the reward obtained. Theory suggests several possible approaches, such as contextual bandits, reinforcement learning, the do-calculus, or plain old Bayesian decision theory. What are the most theoretically appropriate and practical approaches to doing causal inference for interactive systems?
We are particularly interested in case studies of applying machine learning methods to interactive systems that did or did not use Bayesian or likelihood based methods, with a discussion about why this choice was made in terms of practical or theoretical arguments. We also welcome submissions in the following areas:
- Offline evaluation of recommender and interactive systems.
- Comparison of Bayesian, off-policy and other heuristic approaches for offline metrics.
- Probabilistic approaches applied to contextual bandits and reinforcement learning approaches.
- Probabilistic approaches to incrementality and attribution.
- Non-Bayesian approaches and trade-offs with Bayesian/Likelihood approaches.
- Bayesian methods in a production environment.
Organizers
- Nicholas Chopin (ENSAE)
- Mike Gartrell (Criteo AI Lab)
- Dawen Liang (Netflix)
- Alberto Lumbreras (Criteo AI Lab)
- David Rohde (Criteo AI Lab)
- Yixin Wang (UC Berkeley)
August 14 - 18, 2021 (final workshop date TBD)
---
Submission deadline: May 10, 2021, anywhere on Earth
Format: 3 page extended abstract + references + appendices, ACM Proceeding Template
Submission website: https://cmt3.research.microsoft.com/BCIRWIS2021
---
Increasingly we use machine learning to build interactive systems that learn from past actions and the reward obtained. Theory suggests several possible approaches, such as contextual bandits, reinforcement learning, the do-calculus, or plain old Bayesian decision theory. What are the most theoretically appropriate and practical approaches to doing causal inference for interactive systems?
We are particularly interested in case studies of applying machine learning methods to interactive systems that did or did not use Bayesian or likelihood based methods, with a discussion about why this choice was made in terms of practical or theoretical arguments. We also welcome submissions in the following areas:
- Offline evaluation of recommender and interactive systems.
- Comparison of Bayesian, off-policy and other heuristic approaches for offline metrics.
- Probabilistic approaches applied to contextual bandits and reinforcement learning approaches.
- Probabilistic approaches to incrementality and attribution.
- Non-Bayesian approaches and trade-offs with Bayesian/Likelihood approaches.
- Bayesian methods in a production environment.
Organizers
- Nicholas Chopin (ENSAE)
- Mike Gartrell (Criteo AI Lab)
- Dawen Liang (Netflix)
- Alberto Lumbreras (Criteo AI Lab)
- David Rohde (Criteo AI Lab)
- Yixin Wang (UC Berkeley)
